Exploitation of unlabeled sequences in hidden Markov models
Inoue, M.
Ueda, N.
Graduate Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Japan;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Dec. 2003
Volume: 25,
Issue: 12
On page(s): 1570- 1581
ISSN: 0162-8828
INSPEC Accession Number: 7865569
Digital Object Identifier: 10.1109/TPAMI.2003.1251150
Current Version Published: 2003-12-08
Abstract
This paper presents a method for effectively using unlabeled sequential data in the learning of hidden Markov models (HMMs). With the conventional approach, class labels for unlabeled data are assigned deterministically by HMMs learned from labeled data. Such labeling often becomes unreliable when the number of labeled data is small. We propose an extended Baum-Welch (EBW) algorithm in which the labeling is undertaken probabilistically and iteratively so that the labeled and unlabeled data likelihoods are improved. Unlike the conventional approach, the EBW algorithm guarantees convergence to a local maximum of the likelihood. Experimental results on gesture data and speech data show that when labeled training data are scarce, by using unlabeled data, the EBW algorithm improves the classification performance of HMMs more robustly than the conventional naive labeling (NL) approach.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
