Relative Margin Support Tensor Machines for gait and action recognition

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 varoius 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.