PolyU IR
 

PolyU Institutional Repository >
Computing >
COMP Conference Papers & Presentations >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1249

Title: Directional independent component analysis with tensor representation
Authors: Zhang, Lei
Gao, Quanxue
Zhang, David D.
Subjects: Artificial intelligence
Blind source separation
Clustering algorithms
Computational methods
Computer vision
Face recognition
Feature extraction
Hemodynamics
Image processing
Learning algorithms
Pattern recognition
Tensors
Vectors
Issue Date: 2008
Publisher: IEEE
Citation: CVPR '08 : IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, 23-28 June 2008, [p. 1-7].
Abstract: Conventional independent component analysis (ICA) learns the statistical independencies of 2D variables from the training images that are unfolded to vectors. The unfolded vectors, however, make the ICA suffer from the small sample size (SSS) problem that leads to the dimensionality dilemma. This paper presents a novel directional multilinear ICA method to solve those problems by encoding the input image or high dimensional data array as a general tensor. In addition, the mode-k matrix of the tensor is re-sampled and re-arranged to form a mode-k directional image to better exploit the directional information in training. An algorithm called mode-k directional ICA is then presented for feature extraction. Compared with the conventional ICA and other subspace analysis algorithms, the proposed method can greatly alleviate the SSS problem, reduce the computational cost in the learning stage by representing the data in lower dimension, and simultaneously exploit the directional information in the high dimensional dataset. Experimental results on well-known face and palmprint databases show that the proposed method has higher recognition accuracy than many existing ICA, PCA and even supervised FLD schemes while using a low dimension of features.
Rights: © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1249
ISBN: 9781424422432
Appears in Collections:COMP Conference Papers & Presentations

Files in This Item:

File Description SizeFormat
tensor-representation_08.pdf214.03 kBAdobe PDFView/Open



Facebook Facebook del.icio.us del.icio.us LinkedIn LinkedIn


All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
No item in the PolyU IR may be reproduced for commercial or resale purposes.

 

© Pao Yue-kong Library, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Powered by DSpace (Version 1.5.2)  © MIT and HP
Feedback | Privacy Policy Statement | Copyright & Restrictions - Feedback