A Unified View of Matrix Factorization Models
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Algorithms for nonnegative independent component analysis
IEEE Transactions on Neural Networks
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
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In this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of factors in this model will have their cross entropy maximized or minimized. These priors allow us to construct factorization algorithms that result in maximally statistically different factors, something that generic non-negative factorization algorithms cannot explicitly guarantee. We further show how this approach can be used to discover clusters of factors which allow a richer description of data while still effectively performing a low rank analysis.