Hong Jia
ABSTRACT
Feature selection is aimed at reducing the dimensionality of data sets and obtaining a feature subset with better performance for the target learner. Unsupervised feature selection is more challenging because of the lack of label information. In this paper, the idea of decision graph is applied to unsupervised feature selection. Specifically, a 3-D decision graph model of features is proposed to reveal the characteristics of each feature and the relationship between them. Then, a movable hyperplane is constructed to select specified number of features from the original feature space to form the final feature subset. Experiments in comparison with both traditional and novel algorithms on benchmark data sets reveal that the proposed method is able to select feature subset with both of lower redundancy and higher performance.
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Index Terms
- Unsupervised Feature Selection Based on 3-D Feature Decision Graph for High-dimensional Data
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