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Questions tagged [expectation-maximization]

Expectation Maximization (often abbreviated EM) is an iterative algorithm that can be used for maximum likelihood estimation in the presence of missing data or hidden variables.

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What is an intuitive explanation of the Expectation Maximization technique?

Expectation Maximization (EM) is a kind of probabilistic method to classify data. Please correct me if I am wrong if it is not a classifier. What is an intuitive explanation of this EM technique? What is expectation here and what is being…
London guy
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What method do you use for selecting the optimum number of clusters in k-means and EM?

Many algorithms for clustering are available. A popular algorithm is the K-means where, based on a given number of clusters, the algorithm iterates to find best clusters for the objects. What method do you use to determine the number of clusters in…
gd047
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Expectation Maximization coin toss examples

I've been self-studying the Expectation Maximization lately, and grabbed myself some simple examples in the process: http://cs.dartmouth.edu/~cs104/CS104_11.04.22.pdf There are 3 coins 0, 1 and 2 with P0, P1 and P2 probability landing on Head when…
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Viterbi training or Baum-Welch algorithm to estimate the transition and emission probabilities?

I'm trying to find the most probable path (i.e. a sequence of states) on an HMM using the Viterbi algorithm. However, I don't know the transition and emission matrices, which I need to estimate from the observations (data). To estimate these…
dx_mrt
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Any suggestions for how I can plot mixEM type data using ggplot2

I have a sample of 1m records obtained from my original data. (For your reference, you may use this dummy data that may generate approximately similar distribution b <- data.frame(matrix(rnorm(2000000, mean=c(8,17), sd=2))) c <- b[sample(nrow(b),…
rk567
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Numeric example of the Expectation Maximization Algorithm

Could anyone provide a simple numeric example of the EM algorithm as I am not sure about the formulas given? A really simple one with 4 or 5 Cartesian coordinates would perfectly do.
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python Fitting weighted data with Gaussian mixture model (GMM) with minimum on covariance

I want to fit a Gaussian mixture model to a set of weighted data points using python. I tried sklearn.mixture.GMM() which works fine except for the fact that it weights all data points equally. Does anyone know a way to assign weights to the data…
JaneD
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OpenCV: Output of the predict function of Expectation Maximization

Background: I have 2 sets of color pixels from an image, one corresponding to the background, another corresponding to the foreground. Next, I train 2 Gaussian Mixture Models using EM from OpenCV for each set. My aim is to find the probability of a…
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Equidistant points across a cube

I need to initialize some three dimensional points, and I want them to be equally spaced throughout a cube. Are there any creative ways to do this? I am using an iterative Expectation Maximization algorithm and I want my initial vectors to "span"…
sourcenouveau
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Expectation Maximization Issue - How to find the optimum number of gaussians within the data

Is there any algorithm or trick of how to determine the number of gaussians which should be identified within a set of data before applying the expectation maximization algorithm? For example, in the above illustrated plot of 2 - Dimensional data,…
Simon
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iterated conditional mode E step EM

I wanted to know what is the mathematical justification for using ICM as an approximation for the E step in an EM algorithm. As I understand in the E step the idea is to find a distribution that is equal to the posterior distribution of the latent…
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Singular Covariance Matrix in Expectation Maximization

I was trying to code up an EM algorithm in python for clustering images of different types. My understanding of the EM algorithm is as follows: Accordingly, I coded the same in python. Here's my code: import numpy as np import sys from scipy import…
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Is it possible to fit a multivariate GMHMM in hmmlearn?

I know it is possible to fit several sequences into hmmlearn but it seems to me that these sequences need to be drawn from the same distributions. Is it possible to fit a GMHMM with several observations sequences drawn from different distributions…
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Lucene Term Vector Multivariate Bayes Model Expectation Maximization

I am trying to implement an Expectation Maximization algorithm for document clustering. I am planning to use Lucene Term Vectors for finding similarity between 2 documents. There are 2 kinds of EM algos using naive Bayes: the multivariate model…
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kmeans results as initial parameters in Mclust

I am doing Gaussian mixture models. I have done kmeans on the dataset and I want to use the means, variances and the size for the initial parameters for the em algorithm in R. I found that the parameters is a list of 3 and I tried to do the same…
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