Scalable and Flexible Unsupervised Feature Selection

Author:

Hu Haojie1,Wang Rong2,Yang Xiaojun3,Nie Feiping2

Affiliation:

1. Xi'an Research Institute of Hi-Tech, Xi'an 710025, China

2. Center for Optical Imagery Analysis and Learning, Northwestern Polytechnical University, Xi'an 710072, China

3. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China

Abstract

Recently, graph-based unsupervised feature selection algorithms (GUFS) have been shown to efficiently handle prevalent high-dimensional unlabeled data. One common drawback associated with existing graph-based approaches is that they tend to be time-consuming and in need of large storage, especially when faced with the increasing size of data. Research has started using anchors to accelerate graph-based learning model for feature selection, while the hard linear constraint between the data matrix and the lower-dimensional representation is usually overstrict in many applications. In this letter, we propose a flexible linearization model with anchor graph and [Formula: see text]-norm regularization, which can deal with large-scale data sets and improve the performance of the existing anchor-based method. In addition, the anchor-based graph Laplacian is constructed to characterize the manifold embedding structure by means of a parameter-free adaptive neighbor assignment strategy. An efficient iterative algorithm is developed to address the optimization problem, and we also prove the convergence of the algorithm. Experiments on several public data sets demonstrate the effectiveness and efficiency of the method we propose.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

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