Fuzzy clustering based classifier for extraction of individualities from high dimension low sample size data

Article type: Research Article

Authors: Sato-Ilic, Mika

Affiliations: Institute of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8573, Japan | Tel./Fax: +81 29 853 5006; E-mail: [email protected]

Correspondence: [*] Corresponding author: Institute of Systems and Information Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8573, Japan. Tel./Fax: +81 29 853 5006; E-mail: [email protected].

Abstract: The target of this research is high dimension low sample size (HDLSS) data in which the number of variables (or dimensions) is much larger than the number of objects. For such a data, it is well known that mathematically (or statistically), we cannot obtain correct solutions as eigen values of the covariance matrix of variables, therefore, a lot of multivariate analyses cannot apply to this type of data. To overcome this problem, we have proposed a methodology for dimensional reduction, which is a fuzzy cluster scaled principal component analysis (fuzzy cluster scaled PCA). This paper presents a study of the applicability of the previously proposed fuzzy cluster scaled PCA for the discrimination of individual subjects observed by sensors worn on the body during several activities. The analysis of this data is useful for healthcare, considering the individuality of the history of activities, such as the implementation of a custom-made system for healthcare.

Keywords: Fuzzy clustering, HDLSS data, PCA, individuality

DOI: 10.3233/IDT-220226

Journal: Intelligent Decision Technologies, vol. 17, no. 1, pp. 127-138, 2023