Optimal Feature Extraction and Classification of Tensors via Matrix Product State Decomposition

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Conference Proceeding
Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, 2015, pp. 669 - 672
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© 2015 IEEE. Big data consists of large multidimensional datasets that would often be difficult to analyze if working with the original tensor. There is a rising interest in the use of tensor decompositions for feature extraction due to the ability to extract necessary features from a large dimensional feature space. In this paper the matrix product state (MPS) decomposition is used for feature extraction of large tensors. The novelty of the paper is the introduction of a single core tensor obtained from the MPS that not only contains a significantly reduced feature space, but can perform classification with high accuracy without the need of feature selection methods.
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