Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A fast and efficient Modal EM algorithm for Gaussian mixtures   In the modal approach to clustering, clusters are defined as the local maxima
of the underlying probability density function, where the latter can be
estimated either non-parametrically or using finite mixture models. Thus,
clusters are closely related to certain regions around the density modes, and
every cluster corresponds to a bump of the density. The Modal EM algorithm is
an iterative procedure that can identify the local maxima of any density
function. In this contribution, we propose a fast and efficient Modal EM
algorithm to be used when the density function is estimated through a finite
mixture of Gaussian distributions with parsimonious component-covariance
structures. After describing the procedure, we apply the proposed Modal EM
algorithm on both simulated and real data examples, showing its high
flexibility in several contexts.

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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con