Kernel Clustering of Chinese Signature Based on Feature-Weighting
Xiao Dao-ju · Journal of Chinese Computer Systems · 2006
This paper describes features extraction method for five globe features of Chinese signature for classifying signatures of different writers. Feature weighting not selection is selected to reflect the fact that the effect of each feature is not same for classifying the signature when the number of features is small. How to use all samples to get the weight vector in unsupervised method is discussed too. The kernel clustering method using weighted gauss function classifies signatures according to the degree of effect of every feature. Experiments show this weighting-based method improves the right rate of classifying and indicate it is fit to use this method to classify the problem of Chinese signature raised in this paper. The results of experiments show this method is feasible and effective too.