Matrix-Pattern-Oriented Ho-Kashyap Classifier with Regularization Learning

Linear classifier is a common method in statistical pattern recognition. The modified Ho-kashyap with square approximation of the misclassification errors (MHKS) is a linear classifier designed similarly as the support vector machine to maximize the separation margin. However, the conventional linear classifiers are almost based on vector patterns, i.e., before applying them, any non-vector pattern should be first vectorized into a vector pattern. But, such a vectorization will bring three potential problems at least: ①Some implicit structural or contextual information may be corrupted; ②the higher the dimension of input pattern, the more space are needed for storing weight vector; ③When the dimension of pattern is very high and the sample size is small, linear classifier tends to be overstrained. In this paper, a new classifier design method based on matrix patterns, called two-sided linear classifier (MatMHKS), is proposed by modifying the MHKS algorithm. This method can overcome above problems. Experiment results on real dataset show that MatMHKS is more powerful than MHKS.