Robust manifold matrix factorization for joint clustering and feature extraction

Zhang, L; Zhang, Q; Du, B; Tao, D; You, J

Robust manifold matrix factorization for joint clustering and feature extraction

Zhang, L Zhang, Q Du, B Tao, D

You, J

Permalink

Publication Type:: Conference Proceeding
Citation:: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 1662 - 1668
Issue Date:: 2017-01-01

Closed Access

	Filename	Description	Size
	14237-66449-1-PB.pdf	Published version	660.21 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Zhang, Q	en_US
dc.contributor.author	Du, B	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	You, J	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 1662 - 1668	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127227
dc.description.abstract	Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Low-rank matrix approximation has been widely used for data subspace clustering and feature representation in many computer vision and pattern recognition applications. However, in order to enhance the discriminability, most of the matrix approximation based feature extraction algorithms usually generate the cluster labels by certain clustering algorithm (e.g., the kmeans) and then perform the matrix approximation guided by such label information. In addition, the noises and outliers in the dataset with large reconstruction errors will easily dominate the objective function by the conventional ℓ2-norm based squared residue minimization. In this paper, we propose a novel clustering and feature extraction algorithm based on an unified low-rank matrix factorization framework, which suggests that the observed data matrix can be approximated by the production of projection matrix and low dimensional representation, among which the low-dimensional representation can be approximated by the cluster indicator and latent feature matrix simultaneously. Furthermore, we have proposed using the ℓ2,1-norm and integrating the manifold regularization to further promote the proposed model. A novel Augmented Lagrangian Method (ALM) based procedure is designed to effectively and efficiently seek the optimal solution of the problem. The experimental results in both clustering and feature extraction perspectives demonstrate the superior performance of the proposed method.	en_US
dc.relation.ispartof	31st AAAI Conference on Artificial Intelligence, AAAI 2017	en_US
dc.title	Robust manifold matrix factorization for joint clustering and feature extraction	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Low-rank matrix approximation has been widely used for data subspace clustering and feature representation in many computer vision and pattern recognition applications. However, in order to enhance the discriminability, most of the matrix approximation based feature extraction algorithms usually generate the cluster labels by certain clustering algorithm (e.g., the kmeans) and then perform the matrix approximation guided by such label information. In addition, the noises and outliers in the dataset with large reconstruction errors will easily dominate the objective function by the conventional ℓ2-norm based squared residue minimization. In this paper, we propose a novel clustering and feature extraction algorithm based on an unified low-rank matrix factorization framework, which suggests that the observed data matrix can be approximated by the production of projection matrix and low dimensional representation, among which the low-dimensional representation can be approximated by the cluster indicator and latent feature matrix simultaneously. Furthermore, we have proposed using the ℓ2,1-norm and integrating the manifold regularization to further promote the proposed model. A novel Augmented Lagrangian Method (ALM) based procedure is designed to effectively and efficiently seek the optimal solution of the problem. The experimental results in both clustering and feature extraction perspectives demonstrate the superior performance of the proposed method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127227