Matrix factorization-based methods become popular in dyadic data analysis, where a fundamental problem, for example, is to perform document clustering or co-clustering words and documents given a term-document matrix.Expand

We develop Weighted Nonnegative Matrix Co-Tri-Factorization (WNMCTF) where we jointly minimize weighted residuals, each of which involves a nonnegative 3-factor decomposition of target or side information matrix.Expand

We present an algorithm for orthogonal nonnegative matrix factorization, where an orthogonality constraint is imposed on the nonnegative decomposition of a term-document matrix.Expand

We present nonnegative matrix partial co-factorization (NMPCF) where the target matrix (spectrograms of music) and drum-only-matrix (collected from various drums) are simultaneously decomposed, sharing some factor matrix partially, to force some portion of basis vectors to be associated with drums only.Expand

In this paper, we present an algorithm for orthogonal nonnegative matrix factorization, where an orthogonality constraint is imposed on the nonnegative decomposition of a term-document matrix.Expand

We address a problem of separating drum sources from monaural mixtures of polyphonic music containing various pitched instruments as well as drums.Expand

We present a probabilistic model with two dependent latent variables for nonnegative matrix tri-factorization (NMTF) and develop an EM algorithm to learn the model.Expand

We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns.Expand

We present Bayesian matrix co-factorization as an approach to exploiting side information such as content information and demographic user data, where multiple Bayesian decomposition is coupled by sharing some factor matrices.Expand