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dc.contributorDepartment of Computing-
dc.creatorZhang, L-
dc.creatorGao, Q-
dc.creatorZhang, DD-
dc.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.en_US
dc.rightsThis 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.en_US
dc.subjectArtificial intelligenceen_US
dc.subjectBlind source separationen_US
dc.subjectClustering algorithmsen_US
dc.subjectComputational methodsen_US
dc.subjectComputer visionen_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectImage processingen_US
dc.subjectLearning algorithmsen_US
dc.subjectPattern recognitionen_US
dc.titleDirectional independent component analysis with tensor representationen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractConventional 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationCVPR '08 : IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, 23-28 June 2008, [p. 1-7]-
dc.description.ros2007-2008 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
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