Motivation This work addresses two common issues in building classification models for biological or medical studies: learning a sparse model, where only a subset of a large number of possible predictors is used, and training in the presence of missing data. This work focuses on supervised generative binary classification models, specifically linear discriminant analysis (LDA). The parameters a...

We propose an iterative solution to the problem of blindly and jointly identifying the channel response and transmitted symbols in a digital communications system. The proposed algorithm iterates between a symbol estimator, which uses tentative channel estimates to provide soft symbol estimates, and a channel estimator, which uses the symbol estimates to improve the channel estimates. The propo...

This paper elaborates on an efficient approach for clustering discrete data by incrementally building multinomial mixture models through likelihood maximization using the Expectation-Maximization (EM) algorithm. The method adds sequentially at each step a new multinomial component to a mixture model based on a combined scheme of global and local search in order to deal with the initialization p...

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown tha...

We propose an EM-based algorithm to efficiently detect transmitted data in an OFDM system as well as estimating the channel impulse response (CIR). The maximum likelihood estimate of CIR is obtained by using channel statistics (their means and covariances) via the expectation-maximization (EM) algorithm. This algorithm can improve signal detection and the channel estimation accuracy by making u...

This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects to the segmental model. The resulting probabilistic framework provides a basis for learning of waveform models from data as well as parsing and rec...

This paper presented a hybrid optimal estimation algorithm for solving multi-level thresholding problems in image segmentation. The distribution of image intensity is modeled as a random variable, which is approximated by a mixture Gaussian model. The Gaussian’s parameter estimates are iteratively computed by using the proposed PSO + EM algorithm, which consists of two main components: (i) glob...

A fully automated segmentation algorithm for Multiple Sclerosis (MS) lesions is presented. Our method includes two main steps: the detection of lesions by graph cut initialized with a robust Expectation-Maximization (EM) algorithm and the application of rules to remove false positives. Our algorithm will be tested on the ISBI 2015 challenge longitudinal data. For each patient, a unique paramete...

After a couple of disastrous experiments trying to teach EM, we carefully wrote this tutorial to give you an intuitive and mathematically rigorous understanding of EM and why it works. We explain the standard applications of EM to learning Gaussian mixture models (GMMs) and hidden Markov models (HMMs), and prepare you to apply EM to new problems. This tutorial assumes you have an advanced under...

The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed. We propose a practical private EM algorithm that overcomes this challenge using two innovations: (1) a novel moment perturbation formulation for differentially private EM (DP-EM), and (2) the use of two recently devel...