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Relative Margin Support Tensor Machines for gait and action recognition

Published: 05 July 2010 Publication History

Abstract

In this paper, we formulate the Relative Margin Support Tensor Machines (RMSTMs) problem as an extension of the Relative Margin Machines (RMMs). While the typical Support Tensor Machines (STMs) find a solution that is greatly influenced by the data spread, the proposed RMSTMs maximize the margin in a way relative to the spread of the data. The difference in the obtained solutions can be significant in the cases of badly scaled data, especially in the case of various spreads across different data dimensions. The efficiency of the proposed method is illustrated on the problems of gait and action recognition, where the results acquired verify the superiority of the method in terms of classification performance.

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    cover image ACM Conferences
    CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval
    July 2010
    492 pages
    ISBN:9781450301176
    DOI:10.1145/1816041
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 05 July 2010

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    Author Tags

    1. Σ-support tensor machines
    2. Σ-support vector machines
    3. multilinear support tensor machines
    4. relative margin support tensor machines
    5. relative margin support vector machines

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