Multiplicative update rules for Multilinear Support Tensor Machines
Kotsia, Irene and Patras, Ioannis (2010) Multiplicative update rules for Multilinear Support Tensor Machines. In: 20th International Conference on Pattern Recognition (ICPR 2010), 23 - 26 August 2010, Istanbul, Turkey.
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In this paper, we formulate the Multilinear Support Tensor Machines (MSTMs) problem in a similar to the Non-negative Matrix Factorization (NMF) algorithm way. A novel set of simple and robust multiplicative update rules are proposed in order to find the multilinear classifier. Updates rules are provided for both hard and soft margin MSTMs and the existence of a bias term is also investigated. We present results on standard gait and action datasets and report faster convergence of equivalent classification performance in comparison to standard MSTMs.
|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 Research Group
|Depositing User:||Devika Mohan|
|Date Deposited:||03 Dec 2012 06:23|
|Last Modified:||13 Oct 2016 14:25|
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