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Journal ArticleDOI
Convex and Semi-Nonnegative Matrix Factorizations
TL;DR: This work considers factorizations of the form X = FGT, and focuses on algorithms in which G is restricted to containing nonnegative entries, but allowing the data matrix X to have mixed signs, thus extending the applicable range of NMF methods.
Journal ArticleDOI
Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis
TL;DR: Results indicate that IS-NMF correctly captures the semantics of audio and is better suited to the representation of music signals than NMF with the usual Euclidean and KL costs.
Proceedings ArticleDOI
Relational learning via collective matrix factorization
Ajit P. Singh,Geoffrey J. Gordon +1 more
TL;DR: This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.
Book ChapterDOI
Probabilistic Topic Models
TL;DR: Landauer and Dumais as discussed by the authors showed that applying a statistical method such as latent semantic analysis (LSA) to large databases can yield insight into human cognition, and proposed a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.
Journal ArticleDOI
Combinations of muscle synergies in the construction of a natural motor behavior
TL;DR: It is shown that combinations of three time-varying muscle synergies underlie the variety of muscle patterns required to kick in different directions, that the recruitment of these synergies is related to movement kinematics, and that there are similarities among the synergies extracted from different behaviors.
Related Papers (5)
Learning the parts of objects by non-negative matrix factorization
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†
Pentti Paatero,Unto Tapper +1 more