Learning Feature Sparse Principal Components

2019/04/23 by Lai Tian, Tian, Lai, Feiping Nie +3 · 1 citation
Computer Science · Engineering · #Face and Expression Recognition #Sparse and Compressive Sensing Techniques #Advanced Image and Video Retrieval Techniques

paper · pdf · doi:10.48550/arxiv.1904.10155

Abstract

This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization methods for FSPCA require data distribution assumptions and are lack of global convergence guarantee. Though the general FSPCA problem is NP-hard, we show that, for a low-rank covariance, FSPCA can be solved globally (Algorithm 1). Then, we propose another strategy (Algorithm 2) to solve FSPCA for the general covariance by iteratively building a carefully designed proxy. We prove theoretical guarantees on approximation and convergence for the new algorithms. Experimental results show the promising performance of the new algorithms compared with the state-of-the-arts on both synthetic and real-world datasets.

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• Bi-Level Unsupervised Feature Selection

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