University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Sparse subspace clustering via smoothed ℓp minimization

Dong, Wenhua, Wu, Xiao-jun and Kittler, Josef (2019) Sparse subspace clustering via smoothed ℓp minimization Pattern Recognition Letters, 125. pp. 206-211.

[img] Text
Sparse subspace clustering via smoothed ℓp minimization.pdf - Accepted version Manuscript
Restricted to Repository staff only until 23 April 2020.

Download (353kB)

Abstract

In this letter, we formulate sparse subspace clustering as a smoothed ℓp (0 ˂ p ˂ 1) minimization problem (SSC-SLp) and present a unified formulation for different practical clustering problems by introducing a new pseudo norm. Generally, the use of ℓp (0 ˂ p ˂ 1) norm approximating the ℓ0 one can lead to a more effective approximation than the ℓp norm, while the ℓp-regularization also causes the objective function to be non-convex and non-smooth. Besides, better adapting to the property of data representing real problems, the objective function is usually constrained by multiple factors (such as spatial distribution of data and errors). In view of this, we propose a computationally efficient method for solving the multi-constrained non-smooth ℓp minimization problem, which smooths the ℓp norm and minimizes the objective function by alternately updating a block (or a variable) and its weight. In addition, the convergence of the proposed algorithm is theoretically proven. Extensive experimental results on real datasets demonstrate the effectiveness of the proposed method.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Dong, Wenhua
Wu, Xiao-jun
Kittler, JosefJ.Kittler@surrey.ac.uk
Date : 1 July 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1016/j.patrec.2019.04.018
Copyright Disclaimer : © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Sparse subspace clustering; ℓp minimization; Unified formulation; Alternating Direction Method
Depositing User : Clive Harris
Date Deposited : 06 Jun 2019 14:45
Last Modified : 06 Jun 2019 14:45
URI: http://epubs.surrey.ac.uk/id/eprint/851947

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800