
Cutoff for exact recovery of Gaussian mixture models
We determine the cutoff value on separation of cluster centers for exact...
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Clustering SemiRandom Mixtures of Gaussians
Gaussian mixture models (GMM) are the most widely used statistical model...
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Fitting A Mixture Distribution to Data: Tutorial
This paper is a stepbystep tutorial for fitting a mixture distribution...
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How many modes can a constrained Gaussian mixture have?
We show, by an explicit construction, that a mixture of univariate Gauss...
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When Do Birds of a Feather Flock Together? KMeans, Proximity, and Conic Programming
Given a set of data, one central goal is to group them into clusters bas...
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Breathing kMeans
We propose a new algorithm for the kmeans problem which repeatedly incr...
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Parameter Estimation in Gaussian Mixture Models with Malicious Noise, without Balanced Mixing Coefficients
We consider the problem of estimating means of two Gaussians in a 2Gaus...
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KMeans and Gaussian Mixture Modeling with a Separation Constraint
We consider the problem of clustering with Kmeans and Gaussian mixture models with a constraint on the separation between the centers in the context of realvalued data. We first propose a dynamic programming approach to solving the Kmeans problem with a separation constraint on the centers, building on (Wang and Song, 2011). In the context of fitting a Gaussian mixture model, we then propose an EM algorithm that incorporates such a constraint. A separation constraint can help regularize the output of a clustering algorithm, and we provide both simulated and real data examples to illustrate this point.
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