Adaptive discriminant analysis for semi-supervised feature selection

Weichan Zhong, Xiaojun Chen, Feiping Nie, Joshua Zhexue Huang

科研成果: 期刊稿件文章同行评审

38 引用 (Scopus)

摘要

As semi-supervised feature selection is becoming much more popular among researchers, many related methods have been proposed in recent years. However, many of these methods first compute a similarity matrix prior to feature selection, and the matrix is then fixed during the subsequent feature selection process. Clearly, the similarity matrix generated from the original dataset is susceptible to the noise features. In this paper, we propose a novel adaptive discriminant analysis for semi-supervised feature selection, namely, SADA. Instead of computing a similarity matrix first, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative process. Moreover. we introduce the ℓ2,p norm to control the sparsity of S by adjusting p. Experimental results show that S will become sparser with the decrease of p. The experimental results for synthetic datasets and nine benchmark datasets demonstrate the superiority of SADA, in comparison with 6 semi-supervised feature selection methods.

源语言英语
页(从-至)178-194
页数17
期刊Information Sciences
566
DOI
出版状态已出版 - 8月 2021

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