TY - GEN
T1 - Computing Gaussian mixture models with EM using equivalence constraints
AU - Shental, Noam
AU - Bar-Hillel, Aharon
AU - Hertz, Tomer
AU - Weinshall, Daphna
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84898968165&partnerID=8YFLogxK
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AN - SCOPUS:84898968165
SN - 0262201526
SN - 9780262201520
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PB - Neural information processing systems foundation
T2 - 17th Annual Conference on Neural Information Processing Systems, NIPS 2003
Y2 - 8 December 2003 through 13 December 2003
ER -