Learning Heterogeneous Hidden Markov Random Fields

Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page
; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:576-584, 2014.

Abstract

Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is effective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study.

Cite this Paper


BibTeX
@InProceedings{pmlr-v33-liu14, title = {{Learning Heterogeneous Hidden Markov Random Fields}}, author = {Jie Liu and Chunming Zhang and Elizabeth Burnside and David Page}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {576--584}, year = {2014}, editor = {Samuel Kaski and Jukka Corander}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/liu14.pdf}, url = {http://proceedings.mlr.press/v33/liu14.html}, abstract = {Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is effective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study.} }
Endnote
%0 Conference Paper %T Learning Heterogeneous Hidden Markov Random Fields %A Jie Liu %A Chunming Zhang %A Elizabeth Burnside %A David Page %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-liu14 %I PMLR %J Proceedings of Machine Learning Research %P 576--584 %U http://proceedings.mlr.press %V 33 %W PMLR %X Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is effective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study.
RIS
TY - CPAPER TI - Learning Heterogeneous Hidden Markov Random Fields AU - Jie Liu AU - Chunming Zhang AU - Elizabeth Burnside AU - David Page BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics PY - 2014/04/02 DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-liu14 PB - PMLR SP - 576 DP - PMLR EP - 584 L1 - http://proceedings.mlr.press/v33/liu14.pdf UR - http://proceedings.mlr.press/v33/liu14.html AB - Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is effective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study. ER -
APA
Liu, J., Zhang, C., Burnside, E. & Page, D.. (2014). Learning Heterogeneous Hidden Markov Random Fields. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in PMLR 33:576-584

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