关键词: 特征选择, 无监督学习, Schatten-p范数, 特征自表示

Abstract:

Feature selection is to remove the irrelevant and redundant features which aims to find a compact representation of the original features with good generalization ability. Meanwhile, the noise and outliers inhered in data always make the rank of affinity matrix bigger, and result in the learned algorithm cannot catch the truth low rank structure of data. Thus, this paper proposes an unsupervised feature selection algorithm based on Schatten-p norm and feature self-representation（SPSR）, which uses Schatten-p norm to approximate rank minimization problem and feature self-representation to reconstruct affinity matrix of the unsupervised feature selection problem. Furthermore, the SPSR algorithm is solved to select an effective feature subset by using the augmented Lagrangian multipliers and alternating direction multipliers. Finally, compared with several state-of-the-art feature selection methods on six publicly available datasets, SPSR has higher clustering accuracy and effectively identifies the representative feature subset.

Key words: feature selection, unsupervised learning, Schatten-p norm, feature self-representation