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Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?

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Learning Theory (COLT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4539))

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Abstract

Consider the problem of estimating the centers of a uniform mixture of unit-variance spherical Gaussians in ,

(1)

from i.i.d. samples x 1,...,x m drawn from this distribution. This can be done by maximizing the (average log) likelihood . Maximizing the likelihood is guaranteed to recover the correct centers, in the large-sample limit, for any mixture model of the form (1). Unfortunately, maximizing the likelihood is hard in the worst case, and we usually revert to local search heuristics such as Expectation Maximization (EM) which can get trapped in the many local minima the likelihood function might have.

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Authors and Affiliations

  1. Toyota Technological Institute-Chicago IL, USA

    Nathan Srebro

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  1. Nathan Srebro
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Nader H. Bshouty Claudio Gentile

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© 2007 Springer Berlin Heidelberg

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Srebro, N. (2007). Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?. In: Bshouty, N.H., Gentile, C. (eds) Learning Theory. COLT 2007. Lecture Notes in Computer Science(), vol 4539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72927-3_47

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  • DOI: https://doi.org/10.1007/978-3-540-72927-3_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72925-9

  • Online ISBN: 978-3-540-72927-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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