Information bottleneck for non co-occurrence data

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Information bottleneck for non co-occurrence data (2007)

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by Yevgeny Seldin , Noam Slonim , Naftali Tishby

Venue:	In Advances in Neural Information Processing Systems 19
Citations:	12 - 5 self

BibTeX

@INPROCEEDINGS{Seldin07informationbottleneck,
    author = {Yevgeny Seldin and Noam Slonim and Naftali Tishby},
    title = {Information bottleneck for non co-occurrence data},
    booktitle = {In Advances in Neural Information Processing Systems 19},
    year = {2007},
    pages = {1241--1248},
    publisher = {MIT Press}
}

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Abstract

We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X, Y). For example, in gene expression data, the expression level Z is a function of gene X and condition Y; or in movie ratings data the rating Z is a function of viewer X and movie Y. The approach represents a consistent extension of the Information Bottleneck method that has previously relied on the availability of co-occurrence statistics. By altering the relevance variable we eliminate the need in the sample of joint distribution of all input variables. This new formulation also enables simple MDL-like model complexity control and prediction of missing values of Z. The approach is analyzed and shown to be on a par with the best known clustering algorithms for a wide range of domains. For the prediction of missing values (collaborative filtering) it improves the currently best known results. 1

Keyphrases

information bottleneck non co-occurrence data known clustering algorithm input variable information bottleneck method new formulation collaborative filtering co-occurrence statistic expression level simple mdl-like model complexity control joint distribution movie rating general model-independent approach relevance variable wide range consistent extension gene expression data

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