Cédric Févotte
Automatic Relevance Determination in Nonnegative Matrix Factorization with the beta-Divergence.
Mon, 11/28/2011 - 15:01 — SomNambulAuthors: Vincent Y. F. Tan, Cédric Févotte
Subjects: Machine Learning
AbstractThis paper addresses the problem of estimating the latent dimensionality in
nonnegative matrix factorization (NMF). The estimation is done via automatic
relevance determination (ARD). Uncovering the model order is important as it is
necessary to strike the right balance between data fidelity and overfitting. We
propose a Bayesian model for NMF and two families of algorithms known as l1-ARD
and l2-ARD, each assuming different priors on the basis elements and the
activation coefficients.Online algorithms for Nonnegative Matrix Factorization with the Itakura-Saito divergence.
Wed, 06/22/2011 - 15:01 — SomNambulAuthors: Francis Bach, Augustin Lefèvre, Cédric Févotte
Subjects: Machine Learning
AbstractNonnegative matrix factorization (NMF) is now a common tool for audio source
separation. When learning NMF on large audio databases, one major drawback is
that the complexity in time is O(FKN) when updating the dictionary (where (F;N)
is the dimension of the input power spectrograms, and K the number of basis
spectra), thus forbidding its application on signals longer than an hour. We
provide an online algorithm with a complexity of O(FK) in time and memory for
updates in the dictionary.
User login
