Multiresolution Mixture Modeling using Merging of Mixture Components

Prem Raj Adhikari, Jaakko Hollmén
Proceedings of the Asian Conference on Machine Learning, PMLR 25:17-32, 2012.

Abstract

Observing natural phenomena at several levels of detail results in multiresolution data. Extending models and algorithms to cope with multiresolution data is a prerequisite for wide-spread exploitation of the data represented in the multiple resolutions. Mixture models are widely used probabilistic models, however, the mixture models in their standard form can be used to analyze the data represented in a single resolution. In this paper, we propose a multiresolution mixture model based on merging of the mixture components across models represented in different resolutions. Result of such an analysis scenario is to have multiple mixture models, one mixture model for each resolution of data. Our proposed solution is based on the idea on the interaction between mixture models. More specifically, we repeatedly merge component distributions of mixture models across different resolutions. We experiment our proposed algorithm on the two real-world chromosomal aberration datasets represented in two different resolutions. Results show an improvement on the compared multiresolution settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-adhikari12, title = {Multiresolution Mixture Modeling using Merging of Mixture Components}, author = {Adhikari, Prem Raj and Hollmén, Jaakko}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {17--32}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/adhikari12/adhikari12.pdf}, url = {https://proceedings.mlr.press/v25/adhikari12.html}, abstract = {Observing natural phenomena at several levels of detail results in multiresolution data. Extending models and algorithms to cope with multiresolution data is a prerequisite for wide-spread exploitation of the data represented in the multiple resolutions. Mixture models are widely used probabilistic models, however, the mixture models in their standard form can be used to analyze the data represented in a single resolution. In this paper, we propose a multiresolution mixture model based on merging of the mixture components across models represented in different resolutions. Result of such an analysis scenario is to have multiple mixture models, one mixture model for each resolution of data. Our proposed solution is based on the idea on the interaction between mixture models. More specifically, we repeatedly merge component distributions of mixture models across different resolutions. We experiment our proposed algorithm on the two real-world chromosomal aberration datasets represented in two different resolutions. Results show an improvement on the compared multiresolution settings.} }
Endnote
%0 Conference Paper %T Multiresolution Mixture Modeling using Merging of Mixture Components %A Prem Raj Adhikari %A Jaakko Hollmén %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-adhikari12 %I PMLR %P 17--32 %U https://proceedings.mlr.press/v25/adhikari12.html %V 25 %X Observing natural phenomena at several levels of detail results in multiresolution data. Extending models and algorithms to cope with multiresolution data is a prerequisite for wide-spread exploitation of the data represented in the multiple resolutions. Mixture models are widely used probabilistic models, however, the mixture models in their standard form can be used to analyze the data represented in a single resolution. In this paper, we propose a multiresolution mixture model based on merging of the mixture components across models represented in different resolutions. Result of such an analysis scenario is to have multiple mixture models, one mixture model for each resolution of data. Our proposed solution is based on the idea on the interaction between mixture models. More specifically, we repeatedly merge component distributions of mixture models across different resolutions. We experiment our proposed algorithm on the two real-world chromosomal aberration datasets represented in two different resolutions. Results show an improvement on the compared multiresolution settings.
RIS
TY - CPAPER TI - Multiresolution Mixture Modeling using Merging of Mixture Components AU - Prem Raj Adhikari AU - Jaakko Hollmén BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-adhikari12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 17 EP - 32 L1 - http://proceedings.mlr.press/v25/adhikari12/adhikari12.pdf UR - https://proceedings.mlr.press/v25/adhikari12.html AB - Observing natural phenomena at several levels of detail results in multiresolution data. Extending models and algorithms to cope with multiresolution data is a prerequisite for wide-spread exploitation of the data represented in the multiple resolutions. Mixture models are widely used probabilistic models, however, the mixture models in their standard form can be used to analyze the data represented in a single resolution. In this paper, we propose a multiresolution mixture model based on merging of the mixture components across models represented in different resolutions. Result of such an analysis scenario is to have multiple mixture models, one mixture model for each resolution of data. Our proposed solution is based on the idea on the interaction between mixture models. More specifically, we repeatedly merge component distributions of mixture models across different resolutions. We experiment our proposed algorithm on the two real-world chromosomal aberration datasets represented in two different resolutions. Results show an improvement on the compared multiresolution settings. ER -
APA
Adhikari, P.R. & Hollmén, J.. (2012). Multiresolution Mixture Modeling using Merging of Mixture Components. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:17-32 Available from https://proceedings.mlr.press/v25/adhikari12.html.

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