Unsupervised Order-Preserving Regression Kernel for Sequence Analysis

Abstract

In this work, a generalized method for learning from sequence of unlabelled data points based on unsupervised order-preserving regression is proposed. Sequence learning is a fundamental problem, which covers a wide area of research topic including, e.g. handwritten character recognition or speech and natural language processing. For this, one may compute feature vectors from sequence and learn a function in feature space or directly match sequence using methods like dynamic time warping. The former approach is not general in that they rely on sets of application-dependent features, while, in the latter, matching is often inefficient or ineffective. Our method takes the latter approach, while providing a very simple and robust matching. Results obtained from applying our method on a few different types of data show that the method is gerneral, while accuracy is enhanced or comparable.