Title

L1 graph based on sparse coding for feature selection

Document Type

Conference Proceeding

Date of Original Version

1-1-2013

Abstract

In machine learning and pattern recognition, feature selection has been a very active topic in the literature. Unsupervised feature selection is challenging due to the lack of label which would supply the categorical information. How to define an appropriate metric is the key for feature selection. In this paper, we propose a "filter" method for unsupervised feature selection, which is based on the geometry properties of ℓ1 graph. ℓ1 graph is constructed through sparse coding. The graph establishes the relations of feature subspaces and the quality of features is evaluated by features' local preserving ability. We compare our method with classic unsupervised feature selection methods (Laplacian score and Pearson correlation) and supervised method (Fisher score) on benchmark data sets. The classification results based on support vector machine, k-nearest neighbors and multi-layer feed-forward networks demonstrate the efficiency and effectiveness of our method. © 2013 Springer-Verlag Berlin Heidelberg.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7951 LNCS

Issue

PART 1

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