Literature DB >> 35707024

Principal points analysis via p-median problem for binary data.

Haruka Yamashita1, Yoshinobu Kawahara2.   

Abstract

Analysis with principal points is a useful statistical tool for summarizing large data. In this paper, we propose a subgradient-based algorithm to calculate a set of principal points for multivariate binary data by the formulating it as a p-median problem. This enables us to find a globally optimal set of principal points or an ε-optimal solution in the middle of the calculation by combining an upper bound found using the greedy method. This algorithm is an iterative procedure where each iteration can be calculated in an efficient manner. We investigate the applicability of the proposed framework with questionnaire data and arXiv co-authors data.
© 2019 Informa UK Limited, trading as Taylor & Francis Group.

Entities:  

Keywords:  Lagrangian relaxation; Statistical data analysis; principal points; supermodular minimization

Year:  2019        PMID: 35707024      PMCID: PMC9041942          DOI: 10.1080/02664763.2019.1675605

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.416


  2 in total

1.  The p-median model as a tool for clustering psychological data.

Authors:  Hans-Friedrich Köhn; Douglas Steinley; Michael J Brusco
Journal:  Psychol Methods       Date:  2010-03

2.  Principal Point Classification: Applications to Differentiating Drug and Placebo Responses in Longitudinal Studies.

Authors:  Thaddeus Tarpey; Eva Petkova
Journal:  J Stat Plan Inference       Date:  2010-02-01       Impact factor: 1.111

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.