TY - JOUR
T1 - Higher order stationary subspace analysis
AU - Panknin, Danny
AU - Von Bünau, Paul
AU - Kawanabe, Motoaki
AU - Meinecke, Frank C.
AU - Müller, Klaus Robert
N1 - Funding Information:
We would like to acknowledge support for this project by the German Ministry of Education and Research (BMBF) through the project ALICE II (Autonomous Learning in Complex Environments II) (01IB15001B) as well as the Adaptive BCI Project, FKZ 01GQ1115, and by the German Research Foundation (DFG) through the project Co-BCI, Theoretical concepts for co-adaptive human machine interaction with application to BCI (MU987/14-1). This work was also supported by the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant R31-10008. MK was supported in part Japan Science and Technology Agency (German-Japanese cooperation program on computational neuroscience), the Ministry of Education, Culture, Sports, Science and Technology (Grant-in-Aid for Scientific Research B, 24300093)and by the Ministry of Internal Affairs and Communications.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2016/4/6
Y1 - 2016/4/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84964884935&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/699/1/012021
DO - 10.1088/1742-6596/699/1/012021
M3 - Conference article
AN - SCOPUS:84964884935
VL - 699
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012021
T2 - International Meeting on High-Dimensional Data-Driven Science, HD3 2015
Y2 - 14 December 2015 through 17 December 2015
ER -