Sequential Regression Models with Pairwise Constraints Using Noise Clusters
Hengjin Tang and Sadaaki Miyamoto
Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8573, Japan
Switching regression models are useful in a variety of real applications. Semi-supervised clustering with pairwise constraints is also well-known to be important and many researchers recently study this subject. In spite of their usefulness, there is one drawback: the results have a strong dependency on the predefined number of clusters. To avoid this drawback, we use a method of sequentially extracting one cluster at a time using noise-detecting method, and propose constrained switching regressionmodels which enables an automatic determination of clusters. We show the effectiveness of the proposed method by using numerical examples.
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