Probabilistic Models for Incomplete Multi-dimensional Arrays

Wei Chu, Zoubin Ghahramani
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:89-96, 2009.

Abstract

In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-chu09a, title = {Probabilistic Models for Incomplete Multi-dimensional Arrays}, author = {Chu, Wei and Ghahramani, Zoubin}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {89--96}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/chu09a/chu09a.pdf}, url = {https://proceedings.mlr.press/v5/chu09a.html}, abstract = {In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data.} }
Endnote
%0 Conference Paper %T Probabilistic Models for Incomplete Multi-dimensional Arrays %A Wei Chu %A Zoubin Ghahramani %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-chu09a %I PMLR %P 89--96 %U https://proceedings.mlr.press/v5/chu09a.html %V 5 %X In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data.
RIS
TY - CPAPER TI - Probabilistic Models for Incomplete Multi-dimensional Arrays AU - Wei Chu AU - Zoubin Ghahramani BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-chu09a PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 89 EP - 96 L1 - http://proceedings.mlr.press/v5/chu09a/chu09a.pdf UR - https://proceedings.mlr.press/v5/chu09a.html AB - In multiway data, each sample is measured by multiple sets of correlated attributes. We develop a probabilistic framework for modeling structural dependency from partially observed multi-dimensional array data, known as pTucker. Latent components associated with individual array dimensions are jointly retrieved while the core tensor is integrated out. The resulting algorithm is capable of handling large-scale data sets. We verify the usefulness of this approach by comparing against classical models on applications to modeling amino acid fluorescence, collaborative filtering and a number of benchmark multiway array data. ER -
APA
Chu, W. & Ghahramani, Z.. (2009). Probabilistic Models for Incomplete Multi-dimensional Arrays. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:89-96 Available from https://proceedings.mlr.press/v5/chu09a.html.

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