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NIPS
2003

Computing Gaussian Mixture Models with EM Using Equivalence Constraints

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Computing Gaussian Mixture Models with EM Using Equivalence Constraints
Density estimation with Gaussian Mixture Models is a popular generative technique used also for clustering. We develop a framework to incorporate side information in the form of equivalence constraints into the model estimation procedure. Equivalence constraints are defined on pairs of data points, indicating whether the points arise from the same source (positive constraints) or from different sources (negative constraints). Such constraints can be gathered automatically in some learning problems, and are a natural form of supervision in others. For the estimation of model parameters we present a closed form EM procedure which handles positive constraints, and a Generalized EM procedure using a Markov net which handles negative constraints. Using publicly available data sets we demonstrate that such side information can lead to considerable improvement in clustering tasks, and that our algorithm is preferable to two other suggested methods using the same type of side information.
Noam Shental, Aharon Bar-Hillel, Tomer Hertz, Daph
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Noam Shental, Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall
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