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Cognitive Computation
Published in: Cognitive Computation 6/2020

04-09-2020

Feature Selection of Network Data VIA ℓ2,p Regularization

Authors: Ruizhi Zhou, Lingfeng Niu

Published in: Cognitive Computation | Issue 6/2020

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Abstract

Feature selection is the process of selecting a subset of relevant features from the original feature set, and it plays an important role in handling high-dimensional data. In recent years, sparse learning-based feature selection approaches have been widely studied, and different regularizers have been proposed. Among these regularizers, it has been found that ℓ2,p (0 < p < 1) has a good modeling effect on feature selection due to its excellent performance in inducing sparsity. In this paper, we propose the ℓ2,p norm–based feature selection to deal with network data in an unsupervised scenario, and design an iterative algorithm using the framework of the alternating direction method of multipliers. In order to deal with the nonsmooth and non-Lipschitz continuous subproblem caused by ℓ2,p, we design a nonmonotone smoothing trust region algorithm and present its global convergence analysis. The extensive numerical experiments on real-world network datasets validate the effectiveness of the proposed model and algorithm.
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Footnotes
1
https://linqs.soe.ucsc.edu/data
 
2
The dataset is available in the work [23]
 
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Metadata
Title
Feature Selection of Network Data VIA ℓ2,p Regularization
Authors
Ruizhi Zhou
Lingfeng Niu
Publication date
04-09-2020
Publisher
Springer US
Published in
Cognitive Computation / Issue 6/2020
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-020-09763-z

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