Sequence Classification Based on Feature Subsequence Optimized Through Semidefinite Program
The measure of similarity between sequences is of great important to sequence classification.The classifying results could be improved if the similarity is measured through different set of feature subsequences that are determined according to the background and mechanism of sequences.Kernel matrix is defined to measure sequence's similarity on the space spanned by each set of feature subsequences.The combinational optimization of kernels is solved through semidefinite program,which simultaneously makes the classifying distance maximize,therefore a new method for sequence classification is obtained.The new method is used to verify hand written signatures.Experiments on benchmark database show the signature verification accuracy has been enhanced.