Initial value selection for the alternating least squares algorithm

Masahiro Kuroda, Yuichi Mori, Masaya Iizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The alternating least squares (ALS) algorithm is a popular computational algorithm for obtaining least squares solutions minimizing the loss functions in nonlinear multivariate analysis with optimal scaling (NMVA). The ALS algorithm is a simple computational procedure and has a stable convergence property, while the algorithm only guarantees local convergence. In order to avoid finding a local minimum of a loss function, the most commonly used method is to start the ALS algorithm with various random initial values. Such random initialization ALS algorithm tries to find the least squares solution that globally minimizes the loss function. However, the drawback of the random initialization ALS algorithm with multiple runs is to take a huge number of iterations and long computation time. For these problems, we consider initial value selection for selecting an initial value leading to a global minimum of the loss function. The proposed procedure enables efficiently selecting an initial value of the ALS algorithm. Furthermore, we can increase the computation speed when applying the vector ε acceleration for the ALS algorithm to the initial value selection procedure and the least squares estimation in NMVA.

Original languageEnglish
Title of host publicationAdvanced Studies in Classification and Data Science, IFCS 2017
EditorsTadashi Imaizumi, Akinori Okada, Sadaaki Miyamoto, Fumitake Sakaori, Yoshiro Yamamoto, Maurizio Vichi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages227-239
Number of pages13
ISBN (Print)9789811533105
DOIs
Publication statusPublished - 2020
EventBiennial Conference of the International Federation of Classification Societies, IFCS 2017 - Tokyo, Japan
Duration: Aug 8 2017Aug 10 2017

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)1431-8814
ISSN (Electronic)2198-3321

Conference

ConferenceBiennial Conference of the International Federation of Classification Societies, IFCS 2017
CountryJapan
CityTokyo
Period8/8/178/10/17

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Analysis

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