Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Structural Reweight Sparse Subspace Clustering

Structural Reweight Sparse Subspace Clustering Subspace clustering aims to segment a group of data points into a union of subspaces. Reweight sparse subspace clustering is one of the state-of-the-art algorithms which proposed an iterative weighted subspace clustering. The reweight matrix helps to improve the performance of the affinity matrix construction process but it easily falls into a local minimization. In this paper, we propose a structural reweight sparse subspace clustering algorithm which introduces the structural information into reweight subspace clustering. The structural information achieved in spectral clustering process is useful for the subsequent iterative optimization process which helps to obtain a better local minimization. The experimental results on the Extended Yale B, Hopkins 155, and COIL 20 datasets demonstrate that our algorithm achieves a better performance on subspace clustering problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Processing Letters Springer Journals

Structural Reweight Sparse Subspace Clustering

Wang, Ping; Han, Bing; Li, Jie; Gao, Xinbo
Neural Processing Letters , Volume 49 (3) – Jun 1, 2018
Download PDF
13 pages

Loading next page...
 
/lp/springer_journal/structural-reweight-sparse-subspace-clustering-OUR5MisQHB

References (17)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence; Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
DOI
10.1007/s11063-018-9859-8
Publisher site
See Article on Publisher Site

Abstract

Subspace clustering aims to segment a group of data points into a union of subspaces. Reweight sparse subspace clustering is one of the state-of-the-art algorithms which proposed an iterative weighted subspace clustering. The reweight matrix helps to improve the performance of the affinity matrix construction process but it easily falls into a local minimization. In this paper, we propose a structural reweight sparse subspace clustering algorithm which introduces the structural information into reweight subspace clustering. The structural information achieved in spectral clustering process is useful for the subsequent iterative optimization process which helps to obtain a better local minimization. The experimental results on the Extended Yale B, Hopkins 155, and COIL 20 datasets demonstrate that our algorithm achieves a better performance on subspace clustering problem.

Journal

Neural Processing Letters – Springer Journals

Published: Jun 1, 2018

There are no references for this article.