Unsupervised Order-Preserving Regression Kernel for Sequence Analysis

Young-In Shin

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 fundmental problem, which covers a wide area of research topic including, e.g. handwritten character recognition or speech and natural language processing, to name a few. 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 the 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 training and testing take significantly less time, while accuracy is enhanced or comparable.

Subjects: 12. Machine Learning and Discovery; 11. Knowledge Representation


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