Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods an...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 28; no. 6; pp. 1263 - 1275
Main Authors: Zhu, Xiaofeng, Li, Xuelong, Zhang, Shichao, Ju, Chunhua, Wu, Xindong
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coding
Computational modeling
Data models
Design optimization
Dimensionality reduction
Embedding
Encoding
Graphical representations
Learning systems
Linear programming
manifold learning
Manifolds
Objective function
Outliers (statistics)
regression
Robustness
Robustness (mathematics)
sparse coding
State of the art
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2521602