DocumentCode :
1646377
Title :
Temporal factor analysis (TFA): stable-identifiable family, orthogonal flow learning, and automated model selection
Author :
Xu, Lei
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China
Volume :
1
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
472
Lastpage :
476
Abstract :
Temporal factor analysis has been elaborated in a broad scope of the state space model called the stable-identifiable TFA family, and a number of typical situations and the corresponding stability and identifiability have been discussed. Then, orthogonal flow algorithms are suggested for learning, with not only a good numerical property in computation but also an easy implementation of automatic model selection during learning
Keywords :
identification; learning (artificial intelligence); matrix algebra; state-space methods; statistical analysis; automated model selection; identifiability; orthogonal flow learning; stability; stable-identifiable family; state space model; temporal factor analysis; Bayesian methods; Computer science; Control theory; Councils; Gaussian noise; Independent component analysis; Stability analysis; State estimation; State-space methods; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
ISSN :
1098-7576
Print_ISBN :
0-7803-7278-6
Type :
conf
DOI :
10.1109/IJCNN.2002.1005518
Filename :
1005518
Link To Document :
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