TY - GEN
T1 - Stationary subspace analysis as a generalized eigenvalue problem
AU - Hara, Satoshi
AU - Kawahara, Yoshinobu
AU - Washio, Takashi
AU - Von Bünau, Paul
N1 - Funding Information:
This work was partially supported by JSPS Grant-in-Aid for Scientific Research(B) #22300054. We thank Shohei Shimizu for helpful comments.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-642-17537-4_52
DO - 10.1007/978-3-642-17537-4_52
M3 - Conference contribution
AN - SCOPUS:78650224631
SN - 3642175368
SN - 9783642175367
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 422
EP - 429
BT - Neural Information Processing
T2 - 17th International Conference on Neural Information Processing, ICONIP 2010
Y2 - 22 November 2010 through 25 November 2010
ER -