Adaptive Threshold Detection Using Expectation-Maximization Algorithm for Multi-Level Holographic Data Storage

A Hybrid Approach for Data Clustering using Expectation- Maximization and Parameter Adaptive Harmony Search Algorithm

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...

Dynamic Data Rectification Using the Expectation Maximization Algorithm

Although on-line measurements play a ®ital role in process control and monitoring ( process performance, they are corrupted by noise and occasional outliers such as noise ) spikes . Thus, there is a need to rectify the data by remo®ing outliers and reducing noise effects. Well-known techniques such as Kalman Filtering ha®e been used effecti®ely to filter noise measurements, but it is not design...

Expectation Maximization Deconvolution Algorithm

In this paper, we use a general mathematical and experimental methodology to analyze image deconvolution. The main procedure is to use an example image convolving it with a know Gaussian point spread function and then develop algorithms to recover the image. Observe the deconvolution process by adding Gaussian and Poisson noise at different signal to noise ratios. In addition, we will describe ...

The Expectation Maximization Algorithm

This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on TomMinka’s tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition. 1 Intuitive Explanation of EM EM is an iterative optimizationmethod to estimate some unknown ...

Expectation-Maximization (EM) algorithm