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Fitting a
mixture of Gaussians is one of the main
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occupations of
an intellectually shallow field called data-
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mining.
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If we have huge
amounts of data, speed is very
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important. Some
tricks are:
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Initialize
the Gaussians using k-means
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Makes
it easy to get trapped.
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Initialize
K-means using a subset of the datapoints so that
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the
means lie on the low-dimensional manifold.
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Find
the Gaussians near a datapoint more efficiently.
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Use a
KD-tree to quickly eliminate distant Gaussians from
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consideration.
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Fit
Gaussians greedily
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Steal
some mixing proportion from the already fitted
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Gaussians
and use it to fit poorly modeled datapoints better.
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