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Probabilistic latent semantic analysis (PLSA) is a popular topic model for factor analysis of dyadic data, which is closely related to nonnegative matrix factorization (NMF) that seeks a 2-factor decomposition of a nonnegative data matrix. We previously proposed probabilistic matrix tri-factorization (PMTF) which is a probabilistic model for a 3-factor decomposition of a nonnegative data matrix, extending...
Nonnegative matrix tri-factorization (NMTF) is a 3-factor decomposition of a nonnegative data matrix, X ap USVT, where factor matrices, U, S, and V , are restricted to be nonnegative as well. Motivated by the aspect model used for dyadic data analysis as well as in probabilistic latent semantic analysis (PLSA), we present a probabilistic model with two dependent latent variables for NMTF, referred...
A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this paper, we introduce and investigate an alternative...
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