Visualization of Structured Data via Generative Probabilistic Modeling
Similarity-Based Clustering
Modelling Stem Cells Lineages with Markov Trees
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Metric properties of structured data visualizations through generative probabilistic modeling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hidden Markov tree model in dependency-based machine translation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
An EM algorithm to learn sequences in the wavelet domain
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Minimum classification error learning for sequential data in the wavelet domain
Pattern Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Bottom-up generative modeling of tree-structured data
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Journal of Signal Processing Systems
An input-output hidden Markov model for tree transductions
Neurocomputing
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The hidden Markov tree models were introduced by Crouse et al. in 1998 for modeling nonindependent, non-Gaussian wavelet transform coefficients. In their paper, they developed the equivalent of the forward-backward algorithm for hidden Markov tree models and called it the "upward-downward algorithm". This algorithm is subject to the same numerical limitations as the forward-backward algorithm for hidden Markov chains (HMCs). In this paper, adapting the ideas of Devijver from 1985, we propose a new "upward-downward" algorithm, which is a true smoothing algorithm and is immune to numerical underflow. Furthermore, we propose a Viterbi-like algorithm for global restoration of the hidden state tree. The contribution of those algorithms as diagnosis tools is illustrated through the modeling of statistical dependencies between wavelet coefficients with a special emphasis on local regularity changes.