A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization

TL;DR

This paper introduces LDM-NPSTM, a tensor-based binary classifier that preserves data structure, incorporates distribution information, and uses iterative optimization to improve classification accuracy.

Contribution

It proposes a novel tensor classifier combining large margin distribution, nonparallel support tensor machine, and iterative projection optimization, enhancing high-dimensional data classification.

Findings

Improves classification accuracy by preserving tensor structure.

Effectively incorporates marginal distribution information.

Validated through theoretical analysis and numerical experiments.

Abstract

Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM). The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving classification accuracy. Second, this classifier not only considers traditional empirical risk and structural risk but also incorporates the marginal distribution information of the samples, further enhancing its classification performance. To solve this classifier, we use alternative projection algorithm. Specifically, building on the formulation where in the proposed LDM-NPSTM, the parameters defining the separating hyperplane form a tensor (tensorplane) constrained to be the sum of rank-one tensors, the corresponding optimization problem is solved iteratively using alternative projection algorithm. In each iteration, the parameters related to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine-type optimization problem. Finally, the efficiency and performance of the proposed model and algorithm are verified through theoretical analysis and some numerical examples.