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Structure-Aware Classification using Supervised Dictionary Learning
Machine Learning · Submitted 29 Sep 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.
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The abstract, sentence by sentence
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the data points. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. | contribution 0.98 | 0.80 | 0.07 | asserted 0.96 |
| s1 | A graph-based regularization explicitly takes into account the local manifold structure of the data points. | method 0.93 | 0.54 | 0.03 | asserted 1.00 |
| s2 | A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. | method 0.91 | 0.53 | 0.03 | asserted 0.99 |
| s3 | Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. | method 0.99 | 0.30 | 0.03 | asserted 0.65 |
| s4 | The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. | interpretation 0.42 | 0.69 | 0.51 | asserted 0.97 |
| s5 | The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches. | result 1.00 | 0.97 | 0.85 | asserted 1.00 |
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The paper
Original abstract
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the data points. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.
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How the literature uses it
1 arXiv paper cite this in 5 sentences
Papers citing it
Compares with it 1 paper
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Compared with LC-KSVD [ref] , SupGraphDL-L [ref] adopts adaptive graph regularization for dictionary learning and sparse coding individually, and then the enhancement is attained.
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization · Face Recognition · 0.95
Cites it as background 1 paper
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[ref] introduces two adaptive Laplacians for dictionary learning and sparse coding, respectively, and apply them to the single label recognition and multi-label classification.
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization · Related work on the graph Laplacian · 0.86
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