Higher rank support tensor machines

Kotsia, Irene, Guo, Weiwei and Patras, Ioannis (2012) Higher rank support tensor machines. In: 8th International Symposium on Visual Computing (ISVC 2012), 16-18 July, 2012, Crete, Greece.

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Official URL: http://www.isvc.net/12/

Abstract

This work addresses the two class classification problem within the tensor-based large margin classification paradigm. To this end, we formulate the higher rank Support Tensor Machines (STMs), in which the parameters defining the separating hyperplane form a tensor (tensorplane) that is constrained to be the sum of rank one tensors. The corresponding optimization problem is solved in an iterative manner utilizing the CANDECOMP/PARAFAC (CP) decomposition, where at each iteration the parameters corresponding to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine (SVM)-type optimization problem. The efficiency of the proposed method is illustrated on the problems of gait and action recognition where we report results that improve, in some cases considerably, the state of the art.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Intelligent Environments group
Item ID: 9666
Useful Links:
Depositing User: Devika Mohan
Date Deposited: 20 Dec 2012 06:34
Last Modified: 13 Oct 2016 14:25
URI: https://eprints.mdx.ac.uk/id/eprint/9666

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