Handwritten numeral recognition based on nonlinear subspace method
Chi Fang, Xiaoqing Ding, Youshou Wu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
The difficulties of handwritten numeral recognition mainly result from the intrinsic deformations of the handwritten numeral samples. One way to tackle these difficulties is to describe the variations of the feature vectors belonging to one class. Subspace method is a well-known effective pattern recognition method to fulfill this idea. In fact, the subspace method can be embedded into a multivariate linear regression model which response variables are the feature vector and the predictor variables are the principal components (PCs) of the feature vector. When the feature vector is not exactly a Gaussian distribution, it is possible to describe the feature vector more accurately in the sense of least mean squares (LMS) by some nonlinear functions parameterized by the same PCs. This method may result in a higher recognition performance. In this paper we propose an algorithm based on multivariate polynomial regression to fulfill this nonlinear extension. We use the projection pursuit regression (PPR) to determine the multivariate polynomials, in which the polynomial degrees are selected by the structural risk minimization (SRM) method. Experimental results show that our approach is an effective pattern recognition method for the problem of handwritten numeral recognition.