Tensor Distance Based Least Square Twin Support Tensor Machine

Abstract:

Nowadays, there have been many data which are represented by tensor, that how to deal with these tensor data directly remains a significant challenge. In this paper, we propose a new tensor distance (TD) based least square twin support tensor machine (called TDLS-TSTM). Unlike the traditional Euclidean distance, TD considers the relationship information of various coordinates. TDLS-TSTM works directly on tensor data and aims to find two nonparallel hyperplanes for classification based on TD which can make full of structural information of data, solves two systems of linear equations rather than two quadratic programming problems. Compared with other classifiers, our method has the advantages of higher precision and accepted time consumption. The numerical experiments show the valid and efficient of TDLS-TSTM.

• Periodical: Applied Mechanics and Materials (Volumes 668-669)
• Pages: 1170-1173
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.668-669.1170
• Online since: October 2014
• Authors: Hai Fa Shi*, Xin Bin Zhao, Ling Jing
• Keywords: Classification, Least Square Twin Support Tensor Machine, Tensor Distance, Tensor Learning

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