PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Probabilistic models for incomplete multi-dimensional arrays
Wei Chu and Zoubin Ghahramani
In: AISTATS 2009, 16-18 APR 2009, Florida, USA.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6259
Deposited By:Zoubin Ghahramani
Deposited On:08 March 2010