Discriminative and Uncorrelated Feature Selection With Constrained Spectral Analysis in Unsupervised Learning

Xuelong Li, Han Zhang, Rui Zhang, Feiping Nie

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57 引用 (Scopus)

摘要

The existing unsupervised feature extraction methods frequently explore low-redundant features by an uncorrelated constraint. However, the constrained models might incur trivial solutions, due to the singularity of scatter matrix triggered by high-dimensional data. In this paper, we propose a regularized regression model with a generalized uncorrelated constraint for feature selection, which leads to three merits: 1) exploring the low-redundant and discriminative features; 2) avoiding the trivial solutions and 3) simplifying the optimization. Besides that, the local cluster structure is achieved via a novel constrained spectral analysis for the unsupervised learning, where Must-Links and Cannot-Links are transformed into a intrinsic graph and a penalty graph respectively, rather than incorporated into a mixed affinity graph. Accordingly, a discriminative and uncorrelated feature selection with constrained spectral analysis (DUCFS) is proposed with adopting σ-norm regularization for interpolating between F-norm and ℓ2,1-norm. Due to the flexible gradient and global differentiability, our model converges fast. Extensive experiments on benchmark datasets among several state-of-the-art approaches verify the effectiveness of the proposed method.

源语言英语
文章编号8884662
页(从-至)2139-2149
页数11
期刊IEEE Transactions on Image Processing
29
1
DOI
出版状态已出版 - 2020

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