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Conference papers
TENSOR OBJECT CLASSIFICATION VIA MULTILINEAR DISCRIMINANT ANALYSIS NETWORK
Abstract : This paper proposes an multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The MLDANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from decoder. 3) The use of binary hashing and histogram for feature pooling. A learning algorithm for MLDANet is described. Evaluations on UCFll database indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA+LDA, and MLDA in terms of classification for tensor objects.
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01380110
Contributor : Laurent Jonchère Connect in order to contact the contributor
Submitted on : Friday, February 24, 2017 - 3:17:35 PM
Last modification on : Thursday, January 14, 2021 - 11:16:58 AM
Long-term archiving on: : Thursday, May 25, 2017 - 1:04:55 PM
Contributor : Laurent Jonchère Connect in order to contact the contributor
Submitted on : Friday, February 24, 2017 - 3:17:35 PM
Last modification on : Thursday, January 14, 2021 - 11:16:58 AM
Long-term archiving on: : Thursday, May 25, 2017 - 1:04:55 PM
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OBJECT CLASSIFICATION VIA MULT...
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