An efficient Kernel-based matrixized least squares support vector machine

Matrix-pattern-oriented linear classifier design has been proven successful in improving classification performance. This paper proposes an efficient kernelized classifier for Matrixized Least Square Support Vector Machine (MatLSSVM). The classifier is realized by introducing a kernel-induced distance metric and a majority-voting technique into MatLSSVM, and thus is named Kernel-based Matrixized Least Square Support Vector Machine (KMatLSSVM). Firstly, the original Euclidean distance for optimizing MatLSSVM is replaced by a kernel-induced distance, then different initializations for the weight vectors are given and the correspondingly generated sub-classifiers are combined with the majority vote rule, which can expand the solution space and mitigate the local solution of the original MatLSSVM. The experiments have verified that one iteration is enough for each sub-classifier of the presented KMatLSSVM to obtain a superior performance. As a result, compared with the original linear MatLSSVM, the proposed method has significant advantages in terms of classification accuracy and computational complexity.