Abstract
Paper [L. Xu, Temporal BYY learning for state space approach, hidden Markov model and blind source separation, IEEE Trans. Signal Process. 48 (7) (2000) 2132-2144] has presented a temporal factor analysis (TFA) algorithm in state-space model through minimizing an approximate Kullback-divergence cost function. In this paper, we further study the TFA within the maximum-likelihood (ML) framework. Without using any approximation technique, we build a connection between TFA and a traditional filtering problem in control theory, through which a new adaptive TFA algorithm is proposed. This algorithm utilizes the Kalman filter to optimally estimate the states and its covariance matrix, meanwhile using gradient-based method to tune the other model parameters. Furthermore, without taking the measurement noise into account, we further propose a variant of the algorithm and analyze its solution properties accordingly. The numerical simulations have shown promising results.
Original language | English |
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Pages (from-to) | 2966-2980 |
Number of pages | 15 |
Journal | Signal Processing |
Volume | 86 |
Issue number | 10 |
DOIs | |
Publication status | Published - Oct 2006 |
Scopus Subject Areas
- Control and Systems Engineering
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
User-Defined Keywords
- Kalman filter
- State identification
- State-space model
- Temporal factor analysis