Extracting the Optimal Dimensionality for Local Tensor Discriminant Analysis

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Authors Feiping Nie, Shiming Xiang, Yangqiu Song, Changshui Zhang
Journal/Conference Name Pattern Recognition (PR)
Paper Category
Paper Abstract Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor-based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases with respect to the order of tensor, manually selecting the suitable dimensions becomes an impossible task in the case of high-order tensor. In this paper, we aim at solving this important problem and propose an algorithm to extract the optimal dimensionality for local tensor discriminant analysis. Experimental results on a toy example and real-world data validate the effectiveness of the proposed method.
Date of publication 2009
Code Programming Language MATLAB

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