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A kernel method for canonical correlation analysis

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arxiv cs/0609071 v2 pith:PLWKPJBK submitted 2006-09-13 cs.LG cs.CV

Shotaro Akaho This is my paper · ORCID
classification cs.LGcs.CV
keywords methodanalysiscanonicalcorrelationkernelextractfeaturesapplying
verification ladder T0 review T1 audit T2 compute T3 formal

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Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine is an efficient approach to improve such a linear method. In this paper, we investigate the effectiveness of applying kernel method to canonical correlation analysis.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables

    stat.ML 2025-05 conditional novelty 7.0 of 10

    Multi-modal InfoNCE minimizers with temperature optimization provably adapt to the intrinsic dimension of the shared latent variables when aligned maximal-information encoders exist.

  2. Sliding Window Informative Canonical Correlation Analysis

    stat.ML 2025-07 unverdicted novelty 6.0 of 10

    SWICCA extends canonical correlation analysis to streaming data by pairing a streaming PCA backend with a sliding window of samples, supported by simulations and a theoretical performance guarantee.

  3. Task-Driven Common Representation Learning via Bridge Neural Network

    cs.LG 2019-06 unverdicted novelty 6.0 of 10

    Bridge neural network uses two CNNs and negative-sample training to learn task-driven common representations between data sources, asymptotically equivalent to maximizing their total correlation.

  4. Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications

    cs.CV 2025-10 unverdicted novelty 5.0 of 10

    A systematic review that introduces a framework for feature extraction in remote sensing, traces its evolution in the data value chain, and synthesizes trends toward unified representations and foundation models.

  5. Using Contextual Information to Improve Blood Glucose Prediction

    stat.ML 2019-08 conditional novelty 4.0 of 10

    A multi-signal Gaussian process that learns a shared latent representation of blood glucose and contextual data improves next-value glucose prediction on CGM and social media datasets.

  6. Deep Structured Cross-Modal Anomaly Detection

    cs.LG 2019-08 conditional novelty 4.0 of 10

    CMAD learns a shared embedding space for two modalities with a pull-push contrastive loss, then flags instances whose cross-modal similarity falls below a threshold.

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