Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
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 ?/?w STuMs, in order to fully exploit the information offered by the total or the within-class covariance matrix and whiten the data, thus providing invariance 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.
Questions and AnswersYou need to be logged in to be able to post here.