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Article overview
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Unsupervised Feature Analysis with Class Margin Optimization | | Sen Wang
; Feiping Nie
; Xiaojun Chang
; Lina Yao
; Xue Li
; Quan Z. Sheng
; | | Date: |
3 Jun 2015 | | Abstract: | Unsupervised feature selection has been always attracting research attention
in the communities of machine learning and data mining for decades. In this
paper, we propose an unsupervised feature selection method seeking a feature
coefficient matrix to select the most distinctive features. Specifically, our
proposed algorithm integrates the Maximum Margin Criterion with a
sparsity-based model into a joint framework, where the class margin and feature
correlation are taken into account at the same time. To maximize the total data
separability while preserving minimized within-class scatter simultaneously, we
propose to embed Kmeans into the framework generating pseudo class label
information in a scenario of unsupervised feature selection. Meanwhile, a
sparsity-based model, ’ 2 ,p-norm, is imposed to the regularization term to
effectively discover the sparse structures of the feature coefficient matrix.
In this way, noisy and irrelevant features are removed by ruling out those
features whose corresponding coefficients are zeros. To alleviate the local
optimum problem that is caused by random initializations of K-means, a
convergence guaranteed algorithm with an updating strategy for the clustering
indicator matrix, is proposed to iteractively chase the optimal solution.
Performance evaluation is extensively conducted over six benchmark data sets.
From plenty of experimental results, it is demonstrated that our method has
superior performance against all other compared approaches. | | Source: | arXiv, 1506.1330 | | Services: | Forum | Review | PDF | Favorites | |
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