Pith. sign in

REVIEW 5 major objections 5 minor 43 references

An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims CE-MVC, a multi-view clustering framework, outperforms ten existing methods by weighting views with a conditional-entropy score and keeping each view's parameters separate.

desk verdict Promising empirical combination, but Eq (4) mislabels a pairwise sum as conditional entropy; the paper needs a corrected metric and a reproducible release before it merits serious review. read the letter →

arxiv 2412.17647 v1 pith:D5X7AJSH submitted 2024-12-23 cs.AI

Lijian Li This is my paper · ORCID
classification cs.AI
keywords multi-viewclusteringconditionalentropynormalizedmutualinformationadaptiveweightingnoisy-viewdrawbackparameter-decoupleddeepmodelautoencoderrobustness
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

CE-MVC claims that the failure of multi-view clustering in the presence of noisy views can be repaired by treating view quality as a measurable quantity and by keeping each view's network parameters separate. The framework assigns each view a weight from a formula that combines normalized mutual information between the view's soft labels and the unified target with a conditional-entropy score of the view's representation given all other views. Views whose representations carry complementary information receive low conditional entropy and therefore high weight, while noisy views are down-weighted. The paper reports that this scheme beats ten existing clustering methods on DIGIT, COIL, RGB-D, and Caltech, with its largest margins on noise-contaminated versions, and its ablation attributes the gain to adding the conditional-entropy term to exponential NMI weighting.

What carries the argument

The load-bearing object is the weight formula in Eq. (5) together with the conditional-entropy estimate in Eq. (4). Eq. (4) defines the conditional entropy of view $v$ as $\sum_{u\neq v} [E(R^v_t, R^u_t) - E(R^u_t)]$; it is the mechanism that is supposed to quantify complementary information. Eq. (5) turns that score into a weight by dividing an exponential NMI consistency term by the normalized conditional entropy, and the parameter-decoupled autoencoders with reconstruction loss $L^v_r$ and clustering loss $L^v_c$ are the machinery that keeps noisy views from corrupting shared parameters.

What would settle it

A synthetic experiment with three or more views whose dependencies are controlled: choose two informative views that share private information and a third noisy view, compute Eq. (4) for each, and compare the resulting CE-MVC weights and clustering accuracy with known ground-truth informativeness. If a view with high true conditional entropy receives a low score and a low weight, or a genuinely informative view receives a high score, the claim that Eq. (4) quantifies complementarity is refuted. A simpler check is to replace Eq. (4) with the true conditional entropy and see whether CE-MVC's reported margins disappear.

Watch

Extended reading notes

Core claim

At the heart of the paper is Eq. (5), which sets the weight of view $v$ to $$w^v_{(t+1)} = \frac{\exp\!\left(\frac{2M(SL^v; SL_{(t)})}{E(SL^v)+E(SL_{(t)})}\right) - 1}{\mathrm{Norm}\!\left(E(R^v_t \mid \{R^u_t\}_{u\neq v})\right)},$$ where $M$ is mutual information, $E$ is entropy, and the conditional entropy in the denominator is computed by Eq. (4) as the sum over other views $u$ of $E(R^v_t, R^u_t) - E(R^u_t)$. The author's claim is that this ratio simultaneously captures consistency (the NMI-like numerator) and complementarity (the conditional-entropy denominator): a view with information that other views lack has low conditional entropy and is up-weighted, while a mostly redundant or noisy view has high conditional entropy and is down-weighted. Around this weighting rule, CE-MVC trains one autoencoder per view with disjoint parameters, so that clustering and reconstruction losses for each view cannot interfere, and uses the weighted representation to produce the unified soft-label target $T$. The stated result is that this two-part design outperforms the compared methods and is particularly robust to the Noisy-View Drawback.

Load-bearing premise

The weighting scheme assumes that summing pairwise conditional entropies over the other views, as in Eq. (4), faithfully measures how much complementary information a view contributes; for more than two views this is not the true conditional entropy unless an unstated independence condition holds.

Editorial extensions

If this is right

  • CE-MVC is claimed to be the best-performing method on the four tested datasets, with ACC and NMI above 99 percent on COIL and NoisyCOIL.
  • On NoisyDIGIT, CE-MVC improves ACC by 18.8 points and NMI by 20.2 over the best compared method.
  • The ablation study shows that combining exponential NMI with conditional entropy raises ACC on RGB-D from 47.6 to 49.6 and NMI from 40.8 to 42.6, and raises Caltech ACC from 92.5 to 93.8 and NMI from 86.6 to 89.1.
  • The parameter-decoupled design means each view's encoder, decoder, and clustering head are trained independently, so noisy views cannot overfit shared parameters.
  • The weight formula can up-weight a view with low NMI when its conditional entropy is low, addressing the failure mode the paper attributes to NMI-only weighting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable consequence the paper does not state: Eq. (4) equals the true conditional entropy $H(R^v \mid \{R^u\}_{u\neq v})$ only if an independence condition holds across views, and for $V>2$ one can construct pairwise dependencies that make the sum a poor proxy; a Monte Carlo check with controlled dependencies would settle whether the empirical gains survive.
  • The same conditional-entropy weighting could be applied outside clustering, for example to feature fusion in classification or retrieval whenever per-modality representations are learned with separate encoders.
  • Because the weighting is iterative, it may generalize to streaming multi-view data where the set of views changes over time, though the paper does not test this.
  • If the robustness claim holds, the Noisy-View Drawback could be diagnosed in future work by inspecting the conditional-entropy term directly rather than only through final ACC and NMI values.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes CE-MVC, a deep multi-view clustering framework with two main components: (i) an adaptive view-weighting scheme that uses a quantity labeled 'conditional entropy' in Eq. (4) together with an NMI-based term in Eq. (5), and (ii) a parameter-decoupled autoencoder model in which each view has its own encoder, decoder, and clustering head to mitigate the Noisy-View Drawback. Experiments on six datasets (DIGIT, COIL, their noisy versions, RGB-D, and Caltech) report that CE-MVC outperforms ten prior methods, with a small ablation study on weighting strategies.

Significance. If the proposed weighting mechanism were theoretically sound and the empirical results reproducible, CE-MVC would offer a useful step toward robustness against noisy views in deep multi-view clustering, particularly through its parameter-decoupled architecture. The idea of complementing NMI-based consistency weighting with a separate measure of complementarity is reasonable, and the reported gains over strong baselines such as MVCAN on noisy datasets are substantial. However, the paper's central theoretical contribution is not supported as written: the quantity in Eq. (4) is not conditional entropy for V>2, no convergence or monotonicity analysis is given for the weighting iteration, and the experimental protocol lacks key details. The significance is therefore presently limited by these gaps, though the empirical direction may be salvageable with major revision.

major comments (5)
  1. [§II-C, Eq. (4)] Equation (4) defines E(Rv_t | {Ru_t}_{u≠v}) as the sum over u≠v of [E(Rv_t, Ru_t) − E(Ru_t)], which equals the sum of pairwise conditional entropies Σ_{u≠v} H(Rv_t | Ru_t). This is not equal to the true conditional entropy H(Rv_t | {Ru_t}_{u≠v}) unless a conditional-independence condition holds that is never stated. In general, Σ_{u≠v} H(Rv|Ru) ≥ H(Rv|{Ru}_{u≠v}), with equality only when Rv is conditionally independent of each Ru given the other views. The paper also claims, without proof, that a complementary view has relatively low conditional entropy and a noisy view has the highest; no monotonicity or ordering property is established for the pairwise sum. Because this quantity is the denominator in the weighting formula Eq. (5), the central weighting rationale is unsupported as written.
  2. [§II-C, Eq. (5)] Equation (5) contains an undefined normalization operator 'N orm' and an unexplained exponential-NMI numerator. The notation 'N orm(E(Rv_t | {Ru_t}_{u≠v}))' is never defined, making the formula non-reproducible. Moreover, the choice of exp(2M(SLv;SL(t))/(E(SLv)+E(SL(t)))) − 1 is simply a monotone transformation of NMI; no derivation or justification is given for why this specific form should balance consistency and complementarity. Without a precise definition of the normalization, the weights cannot be computed by a reader.
  3. [§II-C, 'Asymptotic adaptive weighting'] The iterative weighting procedure is described as 'asymptotic' but no convergence analysis is provided. There is no theorem, no stopping criterion beyond a generic iteration count, and no discussion of whether the fixed-point iteration W(t+1) = K(...) can oscillate or fail to converge. In addition, Eq. (2) writes R(0) = O(W(0) | R(t), R1, ..., RV), but R(t) does not exist at t=0; this appears to be a typo that obscures the initialization step. The load-bearing claim that the algorithm learns a reliable target T while keeping model parameters fixed needs formal support.
  4. [§II-C, §II-D] There is a self-referential loop in the training procedure: the weights W(t+1) are computed from the same latent representations Rv and soft labels SLv that are produced by the model being trained, and those weights are then used to construct the weighted representation R(t) that generates the clustering target T(t), which in turn supervises the clustering loss in Eq. (6). Since the score and objective co-evolve, the weighting mechanism may simply reflect the model's own current state rather than a meaningful measure of view informativeness. The ablation in Table III compares weighting variants but does not test this circularity, e.g., by using a target derived from an independent source or by freezing the target.
  5. [§III-A and §III-B] The experimental reporting is incomplete. No dataset statistics (number of samples, dimensions, number of views, cluster count) are given; the construction of the noisy views is described only as 'randomly sampled noise' without details. Hyperparameters (λ in Eq. (8), KDE bandwidth, learning rate, optimizer, batch size, number of iterations) are not reported, and no standard deviations or significance tests over repeated runs are provided. No code is released. These omissions prevent verification of the claimed state-of-the-art results and are particularly important because the reported improvements over MVCAN are sometimes small (e.g., 49.6 vs 48.0 ACC on RGB-D).
minor comments (5)
  1. [§II-B, Eq. (1)] Equation (1) defines a common clustering objective but the notation FΘ(SLv | {Rv}_{v=1}^V) is confusing: the soft labels SLv are the output of F, not an input. The equation should be rewritten to show how the shared-parameter fusion takes representations as input and computes labels.
  2. [§II-C, Eq. (2)] The operator O is not defined; it is unclear whether it represents concatenation, a weighted sum, or another aggregation. Also, as noted, the appearance of R(t) in the definition of R(0) is a typo.
  3. [§III-C, Table III] The ablation table compares only NMI, ENMI, and ENMI+CE. There is no condition with NMI+CE alone, so the individual contribution of the conditional-entropy term relative to the exponential transform is not isolated. Also, the table header does not clearly indicate which metric is used in each row.
  4. [Throughout] There are many typographical errors, e.g., 'N orm(repräsentation)' in Eq. (5), 'can’t' in place of 'cannot', and inconsistent use of λ vs. lambda. The paper should be carefully proofread.
  5. [References] Several references in the text, such as [26] for Noisy-View Drawback, are cited in a way that does not clearly connect to the definition. The reference list also contains a mixed style and a few entries with missing page numbers (e.g., [12], [13]).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the adaptive weighting is a heuristic self-training loop, but the empirical evaluation is anchored to external ground-truth labels and no claim reduces to its inputs by construction.

full rationale

The paper's derivation chain contains no step in which a predicted quantity is equivalent to an input by definition or by a fitted parameter. Equation (4) labels a sum of pairwise conditional entropies as the conditional entropy of a view, and for V>2 this label is mathematically inaccurate unless an unstated conditional-independence condition holds; however, this is a correctness or assumption gap, not a circularity. The weighting rule in Eq. (5) is a heuristic schedule: it uses the model's own representations and soft labels, and those weights then reshape the representations used in the next iteration. That is an iterative self-training loop, but it is not a logical reduction of the output to the input by construction, and the final performance claim is validated against external ground-truth labels on DIGIT, COIL, RGB-D, and Caltech, so the empirical comparison is independently grounded. The self-citations in the reference list are contextual background items and are not load-bearing for the adaptive weighting or the parameter-decoupled architecture. No uniqueness theorem is imported from the authors' prior work, no ansatz is smuggled in by citation, and no known result is simply renamed as a new contribution. Therefore, while the theoretical justification of Eq. (4) deserves scrutiny as a validity concern, there is no circular step under the defined criteria.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method rests on four unproven domain assumptions and at least five undisclosed free choices (lambda, KDE bandwidth, iteration count, architecture, optimization settings). There are no invented physical entities.

free parameters (5)
  • lambda (Eq 8)
    Trade-off weight between reconstruction loss and clustering loss; value not reported.
  • KDE bandwidth
    Entropy estimates in Eq (3) require a kernel bandwidth; not specified.
  • number of iterations
    The asymptotic adaptive weighting loop in Section II-C has no stopping criterion or iteration count reported.
  • autoencoder architecture
    Encoder and decoder dimensions, layers, and activations are not specified, so model capacity is an undisclosed free choice.
  • learning rate and optimizer
    Not reported; required to reproduce training.
assumptions (4)
  • ad hoc to paper Eq (4) sum of pairwise entropy differences equals conditional entropy of a view given all other views.
    No independence condition is stated; for V > 2 the equality is false in general, and this equation is the foundation of the weighting scheme.
  • domain assumption Low conditional entropy implies high complementarity and high conditional entropy implies a noisy view.
    Asserted in Section II-C without derivation or independent validation; the entire weighting rule Eq (5) relies on this monotonic relationship.
  • domain assumption KDE-based entropy estimates in Eq (3) are reliable for the high-dimensional latent representations used.
    No bandwidth selection or dimension handling is described; KDE accuracy degrades rapidly with dimension, so this is a nontrivial assumption.
  • domain assumption Parameter-decoupled per-view training prevents the noisy-view drawback without a shared fusion module.
    Motivated intuitively in Section II-D, but no analysis is given to show that independent parameters stop a noisy view from influencing the shared learning target T.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization." pith.science (2026). https://pith.science/paper/D5X7AJSH

@misc{pith2026241217647,
  author       = {Pith},
  title        = {Pith review of: An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5X7AJSH}},
  note         = {Machine review of arXiv:2412.17647}
}
read the original abstract

Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data characterized by multiple perspectives or modalities. Despite significant advancements, existing MVC methods struggle with effectively quantifying the consistency and complementarity among views, and are particularly susceptible to the adverse effects of noisy views, known as the Noisy-View Drawback (NVD). To address these challenges, we propose CE-MVC, a novel framework that integrates an adaptive weighting algorithm with a parameter-decoupled deep model. Leveraging the concept of conditional entropy and normalized mutual information, CE-MVC quantitatively assesses and weights the informative contribution of each view, facilitating the construction of robust unified representations. The parameter-decoupled design enables independent processing of each view, effectively mitigating the influence of noise and enhancing overall clustering performance. Extensive experiments demonstrate that CE-MVC outperforms existing approaches, offering a more resilient and accurate solution for multi-view clustering tasks.

Figures

Figures reproduced from arXiv: 2412.17647 by the authors.

Figure 1
Figure 1. The figure illustrates the visualization for CE-MVC framework’s clustering results on DIGIT and COIL datasets as [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 30 canonical work pages

  1. [1]

    Generalized uncertainty-based evidential fusion with hybrid multi- head attention for weak-supervised temporal action localization

    Yuanpeng He, Lijian Li, Tianxiang Zhan, Wenpin Jiao, and Chi-Man Pun. Generalized uncertainty-based evidential fusion with hybrid multi- head attention for weak-supervised temporal action localization. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 3855–3859. IEEE, 2024

    2024

  2. [2]

    Multiview subspace clustering by an enhanced tensor nuclear norm

    Wei Xia, Xiangdong Zhang, Quanxue Gao, Xiaochuang Shu, Jungong Han, and Xinbo Gao. Multiview subspace clustering by an enhanced tensor nuclear norm. IEEE Trans. Cybern. , 52(9):8962–8975, 2022

    work page 2022

  3. [3]

    A survey on incomplete multiview clustering

    Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, and Jinxing Li. A survey on incomplete multiview clustering. IEEE Trans. Syst. Man Cybern. Syst. , 53(2):1136–1149, 2023

    work page 2023

  4. [4]

    Highly confident local structure based consensus graph learning for incomplete multi-view clustering

    Jie Wen, Chengliang Liu, Gehui Xu, Zhihao Wu, Chao Huang, Lunke Fei, and Yong Xu. Highly confident local structure based consensus graph learning for incomplete multi-view clustering. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023 , pages 15712–15721. IEEE, 2023

    work page 2023

  5. [5]

    Multi- view subspace clustering

    Hongchang Gao, Feiping Nie, Xuelong Li, and Heng Huang. Multi- view subspace clustering. In 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015 , pages 4238–4246. IEEE Computer Society, 2015

    work page 2015

  6. [6]

    Multiview spectral clustering via structured low-rank matrix factorization

    Yang Wang, Lin Wu, Xuemin Lin, and Junbin Gao. Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans. Neural Networks Learn. Syst. , 29(10):4833–4843, 2018

    work page 2018

  7. [7]

    Analyzing the effectiveness and applicability of co-training

    Kamal Nigam and Rayid Ghani. Analyzing the effectiveness and applicability of co-training. In Proceedings of the 2000 ACM CIKM International Conference on Information and Knowledge Management, McLean, VA, USA, November 6-11, 2000 , pages 86–93. ACM, 2000

    work page 2000

  8. [8]

    Representation learning with contrastive predictive coding

    A ¨aron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. CoRR, abs/1807.03748, 2018

    arXiv 2018

Show all 43 references
  1. [9]

    A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions

    Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, and Martin Ester. A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions. CoRR, abs/2206.07579, 2022

    2022 arXiv

  2. [10]

    Deep safe multi-view clustering: Reducing the risk of clustering performance degradation caused by view increase

    Huayi Tang and Yong Liu. Deep safe multi-view clustering: Reducing the risk of clustering performance degradation caused by view increase. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022 , pages 202–211. IEEE, 2022

    2022

  3. [11]

    Deep safe incomplete multi-view clustering: Theorem and algorithm

    Huayi Tang and Yong Liu. Deep safe incomplete multi-view clustering: Theorem and algorithm. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv ´ari, Gang Niu, and Sivan Sabato, editors, International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Balt...

    2022

  4. [12]

    Mmget: a markov model for generalized evidence theory

    Yuanpeng He and Yong Deng. Mmget: a markov model for generalized evidence theory. Computational and Applied Mathematics , 41:1–41, 2022

    2022

  5. [13]

    Tdqmf: Two-dimensional quantum mass function

    Yuanpeng He and Yong Deng. Tdqmf: Two-dimensional quantum mass function. Information Sciences, 621:749–765, 2023

    2023

  6. [14]

    Conflicting management of evidence combination from the point of improvement of basic probability assign- ment

    Yuanpeng He and Fuyuan Xiao. Conflicting management of evidence combination from the point of improvement of basic probability assign- ment. International Journal of Intelligent Systems , 36(5):1914–1942, 2021

    1914

  7. [15]

    Spatio-temporal variability analysis of vege- tation dynamics in china from 2000 to 2022 based on leaf area index: A multi-temporal image classification perspective

    Tianchi Xu, Kai Yan, Yuanpeng He, Si Gao, Kai Yang, Jingrui Wang, Jinxiu Liu, and Zhao Liu. Spatio-temporal variability analysis of vege- tation dynamics in china from 2000 to 2022 based on leaf area index: A multi-temporal image classification perspective. Remote Sensing , 15...

    2000

  8. [16]

    A new base function in basic probability assignment for conflict management

    Yuanpeng He and Fuyuan Xiao. A new base function in basic probability assignment for conflict management. Applied Intelligence, 52(4):4473– 4487, 2022

    2022

  9. [17]

    Nndf: A new neural detection net- work for aspect-category sentiment analysis

    Lijian Li, Yuanpeng He, and Li Li. Nndf: A new neural detection net- work for aspect-category sentiment analysis. In International Conference on Knowledge Science, Engineering and Management , pages 339–355. Springer, 2022

    2022

  10. [18]

    A new distance measure of pythagorean fuzzy sets based on matrix and and its application in medical diagnosis

    Yuanpeng He. A new distance measure of pythagorean fuzzy sets based on matrix and and its application in medical diagnosis. arXiv preprint arXiv:2102.01538, 2021

    2021 arXiv

  11. [19]

    One pass late fusion multi-view clustering

    Xinwang Liu, Li Liu, Qing Liao, Siwei Wang, Yi Zhang, Wenxuan Tu, Chang Tang, Jiyuan Liu, and En Zhu. One pass late fusion multi-view clustering. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 Jul...

    2021

  12. [20]

    Multi-view clustering via late fusion alignment maximization

    Siwei Wang, Xinwang Liu, En Zhu, Chang Tang, Jiyuan Liu, Jingtao Hu, Jingyuan Xia, and Jianping Yin. Multi-view clustering via late fusion alignment maximization. In Sarit Kraus, editor, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence...

    2019

  13. [21]

    Residual feature-reutilization inception network

    Yuanpeng He, Wenjie Song, Lijian Li, Tianxiang Zhan, and Wenpin Jiao. Residual feature-reutilization inception network. Pattern Recognition, page 110439, 2024

    2024

  14. [22]

    Generalized latent multi-view subspace clustering

    Changqing Zhang, Huazhu Fu, Qinghua Hu, Xiaochun Cao, Yuan Xie, Dacheng Tao, and Dong Xu. Generalized latent multi-view subspace clustering. IEEE Trans. Pattern Anal. Mach. Intell. , 42(1):86–99, 2020

    2020

  15. [23]

    Late fusion incomplete multi-view clustering

    Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, Chang Tang, Jianping Yin, Dinggang Shen, Huaimin Wang, and Wen Gao. Late fusion incomplete multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell., 41(10):2410–2423, 2019

    2019

  16. [24]

    Contrastive multi-view kernel learning

    Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Qing Liao, and Yuanqing Xia. Contrastive multi-view kernel learning. IEEE Trans. Pattern Anal. Mach. Intell., 45(8):9552–9566, 2023

    2023

  17. [25]

    Unified one-step multi-view spectral clustering

    Chang Tang, Zhenglai Li, Jun Wang, Xinwang Liu, Wei Zhang, and En Zhu. Unified one-step multi-view spectral clustering. IEEE Trans. Knowl. Data Eng. , 35(6):6449–6460, 2023

    2023

  18. [26]

    Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering

    Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, and Shirui Pan. Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint...

    2016

  19. [27]

    Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi- supervised classification

    Feiping Nie, Jing Li, Xuelong Li, et al. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi- supervised classification. In IJCAI, volume 9, pages 1881–1887, 2016

    2016

  20. [28]

    Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment

    Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, and En Zhu. Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pag...

    2023

  21. [29]

    End-to-end adversarial-attention network for multi-modal clustering

    Runwu Zhou and Yi-Dong Shen. End-to-end adversarial-attention network for multi-modal clustering. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 , pages 14607–14616. Computer Vision Foundation / IEEE, 2020

    2020

  22. [30]

    A multi-view ensemble clustering approach using joint entropy

    Xiaojie Zhao, Xueying Niu, Yang Ma, and Jifu Zhang. A multi-view ensemble clustering approach using joint entropy. Expert Syst. Appl. , 255:124683, 2024

    2024

  23. [31]

    Ordinal belief entropy

    Yuanpeng He and Yong Deng. Ordinal belief entropy. Soft Computing, 27(11):6973–6981, 2023

    2023

  24. [32]

    Entropy-based multi-view matrix completion for clustering with side information

    Changming Zhu and Duoqian Miao. Entropy-based multi-view matrix completion for clustering with side information. Pattern Anal. Appl. , 23(1):359–370, 2020

    2020

  25. [33]

    Entropy weighting based centralized multi-view fuzzy clustering: A case study on brain MR image segmentation

    Hengfei Wu, Guanglei Sheng, and Lin Li. Entropy weighting based centralized multi-view fuzzy clustering: A case study on brain MR image segmentation. J. Medical Imaging Health Informatics, 10(7):1654–1659, 2020

    2020

  26. [34]

    Robustness meets low-rankness: Unified entropy and tensor learning for multi-view subspace clustering

    Shuqin Wang, Yongyong Chen, Zhiping Lin, Yigang Cen, and Qi Cao. Robustness meets low-rankness: Unified entropy and tensor learning for multi-view subspace clustering. IEEE Trans. Circuits Syst. Video Technol., 33(11):6302–6316, 2023

    2023

  27. [35]

    Ordinal fuzzy entropy

    Y He and Y Deng. Ordinal fuzzy entropy. Iranian Journal of Fuzzy Systems, 19(3):171–186, 2022

    2022

  28. [36]

    Generative partial multi-view clustering with adaptive fusion and cycle consistency

    Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu. Generative partial multi-view clustering with adaptive fusion and cycle consistency. IEEE Trans. Image Process. , 30:1771–1783, 2021

    2021

  29. [37]

    Dimc-net: Deep incomplete multi-view clustering network

    Jie Wen, Zheng Zhang, Zhao Zhang, Zhihao Wu, Lunke Fei, Yong Xu, and Bob Zhang. Dimc-net: Deep incomplete multi-view clustering network. In Chang Wen Chen, Rita Cucchiara, Xian-Sheng Hua, Guo- Jun Qi, Elisa Ricci, Zhengyou Zhang, and Roger Zimmermann, editors, MM ’20: The 28th...

    2020

  30. [38]

    Joint deep multi- view learning for image clustering

    Yuan Xie, Bingqian Lin, Yanyun Qu, Cuihua Li, Wensheng Zhang, Lizhuang Ma, Yonggang Wen, and Dacheng Tao. Joint deep multi- view learning for image clustering. IEEE Trans. Knowl. Data Eng. , 33(11):3594–3606, 2021

    2021

  31. [39]

    Yu, and Lifang He

    Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, Xiaorong Pu, Philip S. Yu, and Lifang He. Self-supervised discriminative feature learning for deep multi-view clustering. IEEE Trans. Knowl. Data Eng., 35(7):7470–7482, 2023

    2023

  32. [40]

    Girshick, and Ali Farhadi

    Junyuan Xie, Ross B. Girshick, and Ali Farhadi. Unsupervised deep em- bedding for clustering analysis. In Maria-Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19- 2...

    2016

  33. [41]

    Trosten, Sigurd Løkse, Robert Jenssen, and Michael Kampffmeyer

    Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, and Michael Kampffmeyer. Reconsidering representation alignment for multi-view clustering. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021 , pages 1255–1265. Computer Vision Fo...

    2021

  34. [42]

    Deep incomplete multi-view clustering via mining cluster complementarity

    Jie Xu, Chao Li, Yazhou Ren, Liang Peng, Yujie Mo, Xiaoshuang Shi, and Xiaofeng Zhu. Deep incomplete multi-view clustering via mining cluster complementarity. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 8761–8769, 2022

    2022

  35. [43]

    Investigating and mitigating the side effects of noisy views for self-supervised clustering algorithms in practical multi-view scenarios

    Jie Xu, Yazhou Ren, Xiaolong Wang, Lei Feng, Zheng Zhang, Gang Niu, and Xiaofeng Zhu. Investigating and mitigating the side effects of noisy views for self-supervised clustering algorithms in practical multi-view scenarios. In Proceedings of the IEEE/CVF Conference on Computer...

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.