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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