Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)
Yoram Singer, Manfred K. K. Warmuth
We describe a new iterative method for parameter estimation of Gaus(cid:173) sian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line learning. In contrast to gra(cid:173) dient descent and EM, which estimate the mixture's covariance matrices, the proposed method estimates the inverses of the covariance matrices. Furthennore, the new parameter estimation procedure can be applied in both on-line and batch settings. We show experimentally that it is typi(cid:173) cally faster than EM, and usually requires about half as many iterations as EM.