Support tucker machines
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Official URL: http://dx.doi.org/10.1109/CVPR.2011.5995663
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:||Middlesex University Schools and Centres > School of Science and Technology > Computer Science|
Middlesex University Schools and Centres > School of Science and Technology > Computer Science > Intelligent Environments group
|Deposited On:||27 Nov 2012 06:59|
|Last Modified:||30 Oct 2014 15:00|
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