Semi-supervised Sequential Regression Models

Abstract

Switching regression models are known to be useful in real applications. Semi-supervised clustering is also well-known to be valuable and many researchers study it recently. Although these algorithms are very useful, there is one drawback. The results have a strong dependency on the predefined number of clusters. To avoid this drawback, we apply a method of sequentially extracting one cluster at a time using noise-detecting method to semi-supervised switching regression models which enables an automatic determination of clusters. We show the effectiveness of the proposed method by using numerical examples.