In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Mo...

1 This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number of moment conditions is allowed to increase with the sample size and the moment conditions may be weak. Examples in which these asymptotics are relevant include instrumental variable (IV) estimation with many (possibly weak or uninformed) instruments and some panel data model...

Variability in speech due to dialect is a major factor limiting speech system performance for speech recognition, spoken document retrieval, and dialog systems. In this study, we propose a novel discriminative algorithm to improve dialect classification for unsupervised spontaneous speech in Arabic. No transcripts are used for either training or testing, and all data are spontaneous speech. The...

Generalized Method of Moment (GMM) estimation is one of two developments in econometrics in the 80ies that revolutionized empirical work in macroeconomics. (The other being the understanding of unit roots and cointegration.) The path breaking articles on GMM were those of Hansen (1982) and Hansen and Singleton (1982). For introductions to GMM, Davidson and MacKinnon (1993) have comprehensive ch...

The GMM (generalized min-max) kernel was recently proposed [5] as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the GMM kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GCWS) to convert the GMM kernel to linear kernel. We call this approach as “GMM-GCWS”. In the machine learning l...