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Unsupervised feature selection for visual classification via feature representation property
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DOI：10.1016/j.neucom.2016.07.064.png)
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摘要
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Feature selection is designed to select a subset of features for avoiding the issue of 'curse of dimensionality'. In this paper, we propose a new feature-level self-representation framework for unsupervised feature selection. Specifically, the proposed method first uses a feature-level self-representation loss function to sparsely represent each feature by other features, and then employs an l(2,p)-norm regularization term to yield row-sparsity on the coefficient matrix for conducting feature selection. Experimental results on benchmark databases showed that the proposed method effectively selected the most relevant features than the state-of-the-art methods.
Keyword:
Feature selection
Self-representation
Sparse learning
Unsupervised learning
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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