Stationary subspace analysis as a generalized eigenvalue problem

Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul Von Bünau

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

15 Citations (Scopus)


Understanding non-stationary effects is one of the key challenges in data analysis. However, in many settings the observation is a mixture of stationary and non-stationary sources. The aim of Stationary Subspace Analysis (SSA) is to factorize multivariate data into its stationary and non-stationary components. In this paper, we propose a novel SSA algorithm (ASSA) that extracts stationary sources from multiple time series blocks. It has a globally optimal solution under certain assumptions that can be obtained by solving a generalized eigenvalue problem. Apart from the numerical advantages, we also show that compared to the existing method, fewer blocks are required in ASSA to guarantee the identifiability of the solution. We demonstrate the validity of our approach in simulations and in an application to domain adaptation.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publicationTheory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
Number of pages8
EditionPART 1
Publication statusPublished - 2010
Externally publishedYes
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: Nov 22 2010Nov 25 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on Neural Information Processing, ICONIP 2010
CitySydney, NSW

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Stationary subspace analysis as a generalized eigenvalue problem'. Together they form a unique fingerprint.

Cite this