Support tucker machines

Kotsia, Irene and Patras, Ioannis (2011) Support tucker machines. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), 20 - 25 June 2011, Providence, RI.

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Official URL: http://dx.doi.org/10.1109/CVPR.2011.5995663

Abstract

In this paper we address the two-class classification problem within the tensor-based framework, by formulating the Support Tucker Machines (STuMs). More precisely, in the proposed STuMs the weights parameters are regarded to be a tensor, calculated according to the Tucker tensor decomposition as the multiplication of a core tensor with a set of matrices, one along each mode. We further extend the proposed STuMs to the Σ/Σw STuMs, in order to fully exploit the information offered by the total or the within-class covariance matrix and whiten the data, thus providing in-variance to affine transformations in the feature space. We formulate the two above mentioned problems in such a way that they can be solved in an iterative manner, where at each iteration the parameters corresponding to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine-type problem. The superiority of the proposed methods in terms of classification accuracy is illustrated on the problems of gait and action recognition.

Item Type:Conference or Workshop Item (Paper)
Research Areas:School of Science and Technology > Science & Technology
ID Code:9600
Deposited On:27 Nov 2012 06:59
Last Modified:06 Feb 2013 11:34

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