Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Global Convergence of Least Squares EM for Demixing Two Log-Concave
  Densities   This work studies the location estimation problem for a mixture of two
rotation invariant log-concave densities. We demonstrate that Least Squares EM,
a variant of the EM algorithm, converges to the true location parameter from a
randomly initialized point. We establish the explicit convergence rates and
sample complexity bounds, revealing their dependence on the signal-to-noise
ratio and the tail property of the log-concave distribution. Moreover, we show
that this global convergence property is robust under model mis-specification.
  Our analysis generalizes previous techniques for proving the convergence
results for Gaussian mixtures. In particular, we make use of an
angle-decreasing property for establishing global convergence of Least Squares
EM beyond Gaussian settings, as $\ell_2$ distance contraction no longer holds
globally for general log-concave mixtures.

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The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph