PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Binary Principal Component Analysis in the Netflix Collaborative Filtering Task
Laszlo Kozma, Alexander Ilin and Tapani Raiko
In: 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), 2-4 Sep 2009, Grenoble, France.


We propose an algorithm for binary principal component analysis (PCA) that scales well to very high dimensional and very sparse data. Binary PCA finds components from data assuming Bernoulli distributions for the observations. The probabilistic approach allows for straightforward treatment of missing values. An example application is collaborative filtering using the Netflix data. The results are comparable with those reported for single methods in the literature and through blending we are able to improve our previously obtained best result with PCA.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6401
Deposited By:Tapani Raiko
Deposited On:08 March 2010