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.

Full text is not in this repository.

Official URL:


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:School of Science and Technology > Science & Technology
ID Code:9608
Deposited On:03 Dec 2012 06:23
Last Modified:06 Feb 2013 11:35

Repository staff only: item control page

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year