Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Ordinal principal component analysis is based on the distance between objects and the orthogonal projection of objects into a low dimensional space. The distance between two objects in high dimensional space is larger than the distance between the projected objects. Even if we obtain a very small value of distance between two objects in the projected space the real value of distance between the objects in the observation space may be very large. In order to solve this problem, a weighted principal component analysis has been proposed considering the dissimilarity of objects in the observation space. In the weighted component analysis, a weight function is defined as degree of contribution of each object to a classification structure. In this paper, we propose various weight functions and investigate the features.
