Generalized Similarity Kernels for Efficient Sequence Classification

String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on tasks such as document topic elucidation, music genre classification, protein superfamily and fold prediction. However, typical string kernel methods rely on symbolic Hammingdistance based matching which may not necessarily reflect the underlying (e.g., physical) similarity between sequence fragments. In this work we propose a novel computational framework that uses general similarity metrics S(·, ·) and distance-preserving embeddings with string kernels to improve sequence classification. In particular, we consider two approaches that allow one either to incorporate non-Hamming similarity S(·, ·) into similarity evaluation by matching only the features that are similar according to S(·, ·) or to retain actual (approximate) similarity/distance scores in similarity evaluation. An embedding step, a distance-preserving bitstring mapping, is used to effectively capture similarity between otherwise symbolically different sequence elements. We show that it is possible to retain computational efficiency of string kernels while using this more “precise” measure of similarity. We then demonstrate that on a number of sequence classification tasks such as music, and biological sequence classification, the new method can substantially improve upon state-of-the-art string kernel baselines.