Group sparse feature selection on local learning based clustering
NEUROCOMPUTING(2016)
摘要
Feature selection plays an important role in many machine learning applications. By extracting meaningful features and eliminating both redundancies and noises, it effectively improves the accuracy and efficiency of the learning algorithm. In this paper, an unsupervised feature selection method called GSFS-llc (group sparse feature selection on local learning based clustering) is proposed. GSFS-llc first retrieves the cluster structure in a dataset using LLC and then selects important features that best preserve the cluster structure by L-2,L-1-norm regularized regression. By combining group sparsity regularization with locality based learning algorithm, GSFS-llc leads a more robust result than other methods and selects features that respect the underlying geometric structure in the dataset. Extensive experimental results over real world handwritten digits, human faces, voices and object images datasets demonstrate the superiority of the GSFS-llc algorithm. (C) 2015 Elsevier B.V. All rights reserved.
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关键词
Unsupervised,Group sparsity,Local learning,Feature selection
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