Higher order stationary subspace analysis

International Meeting on High-Dimensional Data-Driven Science 2015

Panknin, Danny^1 ; Von Bünau, Paul^1 ; Kawanabe, Motoaki^2 ; Meinecke, Frank C.^1 ; Müller, Klaus-Robert^1

Berlin Institute of Technology (TU Berlin), Machine Learning Group, Computer Science, Germany^1

Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto

619-0288, Japan^2

关键词: Higher moments;    Higher order moments;    Higher-order;    Limited data;    Non-stationarities;    Nonstationary;    Second moments;    Subspace analysis;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/699/1/012021/pdf
DOI  :  10.1088/1742-6596/699/1/012021

来源: IOP

【 摘 要 】

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.