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.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 2016 Apr 6|
|Event||International Meeting on High-Dimensional Data-Driven Science, HD3 2015 - Kyoto, Japan|
Duration: 2015 Dec 14 → 2015 Dec 17
ASJC Scopus subject areas
- Physics and Astronomy(all)