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A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
[article]
2018
arXiv
pre-print
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on the short-time Fourier or Cosine transforms, the ability to learn transforms from raw data attracts increasing attention. However, this adds an important computational overhead. When assumed orthogonal (like the Fourier or Cosine transforms), learning the transform yields a non-convex optimization problem on the
arXiv:1811.02225v1
fatcat:r2tjpq7k4jh2fdhxj4apvb355a