Complexity reduction in fixed-lag smoothing for hidden Markov models

Author

Shue, Louis ; Dey, Subhrakanti

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

50

Issue

5

fYear

2002

fDate

5/1/2002 12:00:00 AM

Firstpage

1124

Lastpage

1132

Abstract

We investigate approximate smoothing schemes for a class of hidden Markov models (HMMs), namely, HMMs with underlying Markov chains that are nearly completely decomposable. The objective is to obtain substantial computational savings. Our algorithm can not only be used to obtain aggregate smoothed estimates but can be used also to obtain systematically approximate full-order smoothed estimates with computational savings and rigorous performance guarantees, unlike many of the aggregation methods proposed earlier

Keywords

Kalman filters; communication complexity; hidden Markov models; signal processing; smoothing methods; HMM; Kalman filtering; Markov chains; aggregate smoothed estimates; approximate full-order smoothed estimates; complexity reduction; computational savings; fixed-lag smoothing; hidden Markov models; performance guarantees; signal processing; Aggregates; Application software; Biological system modeling; Biomedical signal processing; Filtering; Hidden Markov models; Signal processing algorithms; Smoothing methods; Speech recognition; State estimation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.995068

Filename

995068