Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Quasi-Newton algorithm on the orthogonal manifold for NMF with
  transform learning   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 orthogonal matrix manifold. In this
paper, we derive a quasi-Newton method on the manifold using sparse
approximations of the Hessian. Experiments on synthetic and real audio data
show that the proposed algorithm out-performs state-of-the-art first-order and
coordinate-descent methods by orders of magnitude. A Python package for fast
TL-NMF is released online at https://github.com/pierreablin/tlnmf.

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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th