Robust structure low-rank representation in latent space

Congzhe You, Vasile Palade, Xiao-Jun Wu

Research output: Contribution to journalArticle

Abstract

Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction for subspace clustering. The existing LRR and its extensions use noise data as the dictionary, while this influences the final clustering results. The method proposed in this paper uses a discriminant dictionary for matrix recovery and completion in order to find the lowest rank representation of the data matrix. As the algorithm performs clustering operations in low-dimensional latent space, the computational efficiency of the algorithm is higher, which is also a major advantage of the proposed algorithm in this paper. A large number of experiments on standard datasets show the efficiency and effectiveness of the proposed method in subspace clustering problems.
Original languageEnglish
Pages (from-to)117-124
Number of pages8
JournalEngineering Applications of Artificial Intelligence
Volume77
Early online date26 Oct 2018
DOIs
Publication statusPublished - Jan 2019

Fingerprint

Glossaries
Clustering algorithms
Computational efficiency
Computer vision
Recovery
Processing
Experiments

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the
publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.
A definitive version was subsequently published in Engineering Applications of
Artificial Intelligence, Vol 77, (2019) DOI: 10.1016/j.engappai.2018.09.008
© 2017, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Graph regularization
  • Image processing
  • Latent space
  • Low-rank representation
  • Subspace clustering

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Robust structure low-rank representation in latent space. / You, Congzhe; Palade, Vasile; Wu, Xiao-Jun.

In: Engineering Applications of Artificial Intelligence, Vol. 77, 01.2019, p. 117-124.

Research output: Contribution to journalArticle

@article{f8a602a504ca477a9186e2aee2cd9dcb,
title = "Robust structure low-rank representation in latent space",
abstract = "Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction for subspace clustering. The existing LRR and its extensions use noise data as the dictionary, while this influences the final clustering results. The method proposed in this paper uses a discriminant dictionary for matrix recovery and completion in order to find the lowest rank representation of the data matrix. As the algorithm performs clustering operations in low-dimensional latent space, the computational efficiency of the algorithm is higher, which is also a major advantage of the proposed algorithm in this paper. A large number of experiments on standard datasets show the efficiency and effectiveness of the proposed method in subspace clustering problems.",
keywords = "Graph regularization, Image processing, Latent space, Low-rank representation, Subspace clustering",
author = "Congzhe You and Vasile Palade and Xiao-Jun Wu",
note = "NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, Vol 77, (2019) DOI: 10.1016/j.engappai.2018.09.008 {\circledC} 2017, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",
year = "2019",
month = "1",
doi = "10.1016/j.engappai.2018.09.008",
language = "English",
volume = "77",
pages = "117--124",
journal = "Engineering Applications of Artificial Intelligence",
issn = "0952-1976",
publisher = "Elsevier",

}

TY - JOUR

T1 - Robust structure low-rank representation in latent space

AU - You, Congzhe

AU - Palade, Vasile

AU - Wu, Xiao-Jun

N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, Vol 77, (2019) DOI: 10.1016/j.engappai.2018.09.008 © 2017, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2019/1

Y1 - 2019/1

N2 - Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction for subspace clustering. The existing LRR and its extensions use noise data as the dictionary, while this influences the final clustering results. The method proposed in this paper uses a discriminant dictionary for matrix recovery and completion in order to find the lowest rank representation of the data matrix. As the algorithm performs clustering operations in low-dimensional latent space, the computational efficiency of the algorithm is higher, which is also a major advantage of the proposed algorithm in this paper. A large number of experiments on standard datasets show the efficiency and effectiveness of the proposed method in subspace clustering problems.

AB - Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction for subspace clustering. The existing LRR and its extensions use noise data as the dictionary, while this influences the final clustering results. The method proposed in this paper uses a discriminant dictionary for matrix recovery and completion in order to find the lowest rank representation of the data matrix. As the algorithm performs clustering operations in low-dimensional latent space, the computational efficiency of the algorithm is higher, which is also a major advantage of the proposed algorithm in this paper. A large number of experiments on standard datasets show the efficiency and effectiveness of the proposed method in subspace clustering problems.

KW - Graph regularization

KW - Image processing

KW - Latent space

KW - Low-rank representation

KW - Subspace clustering

UR - http://www.scopus.com/inward/record.url?scp=85055491113&partnerID=8YFLogxK

U2 - 10.1016/j.engappai.2018.09.008

DO - 10.1016/j.engappai.2018.09.008

M3 - Article

VL - 77

SP - 117

EP - 124

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

SN - 0952-1976

ER -