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Yaroslav Bulatov
Yaroslav Bulatov
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This year's FOCS paper seems relevant.

"Settling the Polynomial Learnability of Mixtures of Gaussians"

Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians.

Edit 10/25: Suresh has a nice summary of the two papers that appeared on this problem here http://geomblog.blogspot.com/2010/10/focs-day-1-clustering.html

This year's FOCS paper seems relevant.

"Settling the Polynomial Learnability of Mixtures of Gaussians"

Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians.

This year's FOCS paper seems relevant.

"Settling the Polynomial Learnability of Mixtures of Gaussians"

Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians.

Edit 10/25: Suresh has a nice summary of the two papers that appeared on this problem here http://geomblog.blogspot.com/2010/10/focs-day-1-clustering.html

Source Link
Yaroslav Bulatov
Yaroslav Bulatov
  • 2.4k
  • 21
  • 40

This year's FOCS paper seems relevant.

"Settling the Polynomial Learnability of Mixtures of Gaussians"

Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians.