TWin support tensor machines for MCs detection

Abstract

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition. This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier. In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine (TWSVM) to the tensor-based method TWin Support Tensor Machines (TWSTM), which accepts general tensors as input. To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters (MCs) detection. In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not. A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm. By comparison with TWSVM, the tensor version reduces the overfitting problem.