Matrix-based Neural Network with Linear Nodes

Abstract

In this article we propose a new linear model for regression/classification of matrix input data. The algorithm for parameter estimation is constructed, some properties of the model are analyzed. The proposed model was applied for various binary classification problems, experimentally demonstrated, that in the case of small training sample, this model can be more efficient than standard techniques. In each experiment the training sample was selected randomly, the results (correct classification probabilities on the testing set) were averaged, statistical hypothesis about efficiency of the models were tested. By signed rank test most of the results are statistically significant. Bibl. 9 (in English; summaries in English, Russian and Lithuanian).