Title :
Temporal factor analysis (TFA): stable-identifiable family, orthogonal flow learning, and automated model selection
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005518