Higher order stationary subspace analysis

Danny Panknin, Paul Von Bünau, Motoaki Kawanabe, Frank C. Meinecke, Klaus Robert Müller

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


Non-stationarity in data is an ubiquitous problem in signal processing. The recent stationary subspace analysis procedure (SSA) has enabled to decompose such data into a stationary subspace and a non-stationary part respectively. Algorithmically only weak non- stationarities could be tackled by SSA. The present paper takes the conceptual step generalizing from the use of first and second moments as in SSA to higher order moments, thus defining the proposed higher order stationary subspace analysis procedure (HOSSA). The paper derives the novel procedure and shows simulations. An obvious trade-off between the necessity of estimating higher moments and the accuracy and robustness with which they can be estimated is observed. In an ideal setting of plenty of data where higher moment information is dominating our novel approach can win against standard SSA. However, with limited data, even though higher moments actually dominate the underlying data, still SSA may arrive on par.

Original languageEnglish
Article number012021
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2016 Apr 6
EventInternational Meeting on High-Dimensional Data-Driven Science, HD3 2015 - Kyoto, Japan
Duration: 2015 Dec 142015 Dec 17

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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