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First introduced in 1977 Lots of mathematical derivation Problem : given a set of data (data is incomplete or having missing values). Goal : assume the set of data come from a underlying distribution, we need to guess the most likely (maximum likelihood) parameters of that model. Expectation Maximization

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Given a set of data points in R 2 Assume underlying distribution is mixture of Gaussians Goal: estimate the parameters of each gaussian distribution Ѳ is the parameter, we consider it consists of means and variances, k is the number of Gaussian model. Example

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Steps of EM algorithm(1) randomly pick values for Ѳ k (mean and variance) for each x n, associate it with a responsibility value r r n,k - how likely the n th point comes from/belongs to the k th mixture how to find r? Assume data come from these two distribution

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Probability that we observe x n in the data set provided it comes from k th mixture Steps of EM algorithm(2) Distribution by Ѳ k Distance between x n and center of k th mixture

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Steps of EM algorithm(3) each data point now associate with (r n,1, r n,2,…, r n,k ) r n,k – how likely they belong to k th mixture, 0<r<1 using r, compute weighted mean and variance for each gaussian model We get new Ѳ, set it as the new parameter and iterate the process (find new r -> new Ѳ -> ……) Consist of expectation step and maximization step

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Ideas and Intuition given a set of incomplete (observed) data assume observed data come from a specific model formulate some parameters for that model, use this to guess the missing value/data (expectation step) from the missing data and observed data, find the most likely parameters (maximization step) iterate step 2,3 and converge

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Application Parameter estimation for Gaussian mixture (demo)demo Baum-Welsh algorithm used in Hidden Markov Models Difficulties How to model the missing data? How to determine the number of Gaussian mixture. What model to be used?

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