Title: The analysis based on principal matrix decomposition for 3-mode binary data

Authors: Haruka Yamashita; Masayuki Goto

Addresses: Department of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, Japan ' Department of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, Japan

Abstract: Recently, principal points for a multivariate binary distribution (Yamashita and Suzuki, 2014, 2015) have been proposed as the binary vectors that optimally represent a distribution, in terms of the average Euclidian squared distance between a multivariate binary distribution and the vectors. In this paper, we proposes a new analysis procedure for 3-mode binary data, based on principal points for a multivariate binary distribution (Yamashita and Suzuki, 2014, 2015). Moreover, we propose a method that decomposes principal matrixes for 3-mode binary data into a small number of vectors based on vector products. In order to investigate our method's applicability to real-world data, we use the method to analyse 3-mode structured data from annual all-star games for Japanese professional baseball.

Keywords: principal points; 3-mode data; binary data; clustering; data analysis.

DOI: 10.1504/AJMSA.2017.083504

Asian Journal of Management Science and Applications, 2017 Vol.3 No.1, pp.24 - 37

Received: 02 Mar 2016
Accepted: 08 Sep 2016

Published online: 08 Apr 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article