Structural Reweight Sparse Subspace Clustering

Structural Reweight Sparse Subspace Clustering Neural Process Lett https://doi.org/10.1007/s11063-018-9859-8 1 1 1 1 Ping Wang · Bing Han · Jie Li · Xinbo Gao © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Subspace clustering aims to segment a group of data points into a union of sub- spaces. 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 mini- mization. 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 opti- mization 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. Keywords Sparse subspace clustering · Motion segmentation · Structural information 1 Introduction In many real-world applications, plenty of high-dimensional datasets need to be segmented into low-dimensional subspaces, e.g., computer vision, machine learning and image process- ing. Because the high-dimensional data processing http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Processing Letters Springer Journals

Structural Reweight Sparse Subspace Clustering

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence
ISSN
1370-4621
eISSN
1573-773X
D.O.I.
10.1007/s11063-018-9859-8
Publisher site
See Article on Publisher Site

Abstract

Neural Process Lett https://doi.org/10.1007/s11063-018-9859-8 1 1 1 1 Ping Wang · Bing Han · Jie Li · Xinbo Gao © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Subspace clustering aims to segment a group of data points into a union of sub- spaces. 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 mini- mization. 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 opti- mization 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. Keywords Sparse subspace clustering · Motion segmentation · Structural information 1 Introduction In many real-world applications, plenty of high-dimensional datasets need to be segmented into low-dimensional subspaces, e.g., computer vision, machine learning and image process- ing. Because the high-dimensional data processing

Journal

Neural Processing LettersSpringer Journals

Published: Jun 1, 2018

References

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