Element-Level Clustering of Feature Vectors Considering Correlations for Analyzing Image Data
Masako Omachi, Shinichiro Omachi · 2016
Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements, the correlation is used as the metric for clustering. In order to effectively solve the optimization problem, a technique for graph cut is used. After dividing the set of elements into subsets, clustering is performed for each subset. Experiments using a handwritten image database show the effectiveness of the proposed method.