Expectation–maximization algorithm

About: Expectation–maximization algorithm is a research topic. Over the lifetime, 11823 publications have been published within this topic receiving 528693 citations. The topic is also known as: EM algorithm & Expectation Maximization.

A Spectral Algorithm for Latent Tree Graphical Models

Abstract: Latent variable models are powerful tools for probabilistic modeling, and have been successfully applied to various domains, such as speech analysis and bioinformatics. However, parameter learning algorithms for latent variable models have predominantly relied on local search heuristics such as expectation maximization (EM). We propose a fast, local-minimum-free spectral algorithm for learning latent variable models with arbitrary tree topologies, and show that the joint distribution of the observed variables can be reconstructed from the marginals of triples of observed variables irrespective of the maximum degree of the tree. We demonstrate the performance of our spectral algorithm on synthetic and real datasets; for large training sizes, our algorithm performs comparable to or better than EM while being orders of magnitude faster.

A predominant-F/sub 0/ estimation method for CD recordings: MAP estimation using EM algorithm for adaptive tone models

Abstract: This paper describes a predominant-F/sub 0/ (fundamental frequency) estimation method called PreFEst, which can detect melody and bass lines in monaural audio signals containing sounds of various instruments, While most previous methods premised mixtures of a few sounds and had difficulty dealing with such complex signals, our method can estimate the F/sub 0/ of the melody and bass lines without assuming the number of sound sources in compact-disc recordings. In this paper we propose the following three extensions to our previous PreFEst to make it more adaptive and flexible: introducing multiple harmonic-structure tone models, estimating the shape of tone models, and introducing a prior distribution of its shape and F/sub 0/ estimates These extensions were implemented by the MAP (maximum a posteriori probability) estimation by using the expectation-maximization algorithm. Experimental results with compact-disc recordings showed that our real-time system based on the extended PreFEst achieved performance improvement.

Inference for normal mixtures in mean and variance

Abstract: A finite mixture of normal distributions, in both mean and variance parameters, is a typical finite mixture in the location and scale families Because the likelihood function is unbounded for any sample size, the ordinary maximum likelihood estimator is not consistent Applying a penalty to the likelihood function to control the estimated component variances is thought to restore the optimal properties of the likelihood approach Yet this proposal lacks practical guidelines, has not been indisputably justified, and has not been investigated in the most general setting In this paper, we present a new and solid proof of consistency when the putative number of components is equal to, and when it is larger than, the true number of components We also provide conditions on the required size of the penalty and study the invariance properties The finite sample properties of the new estimator are also demonstrated through simulations and an example from genetics

Maximum likelihood noise cancellation using the EM algorithm

Abstract: An approach to the two-microphone speech enhancement problem is discussed. Specifically, a maximum-likelihood (ML) problem is formulated for estimating the parameters needed for canceling the noise, and solved by the iterative EM (estimate-maximize) technique. The EM algorithm has been implemented for both a simplified and a more general scenario. The results improve upon those obtained with the classical least-squares approach. >

Mixture model analysis of DNA microarray images

Abstract: In this paper, we propose a new methodology for analysis of microarray images. First, a new gridding algorithm is proposed for determining the individual spots and their borders. Then, a Gaussian mixture model (GMM) approach is presented for the analysis of the individual spot images. The main advantages of the proposed methodology are modeling flexibility and adaptability to the data, which are well-known strengths of GMM. The maximum likelihood and maximum a posteriori approaches are used to estimate the GMM parameters via the expectation maximization algorithm. The proposed approach has the ability to detect and compensate for artifacts that might occur in microarray images. This is accomplished by a model-based criterion that selects the number of the mixture components. We present numerical experiments with artificial and real data where we compare the proposed approach with previous ones and existing software tools for microarray image analysis and demonstrate its advantages.