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1 k-mean vs GMM
Give a variant of k-mean algorithm somewhat between the original k-mean and ExpectationMaximization (EM) for Gaussian Mixture Models (GMM). Please specify the computational details of the formulas. Pseudo-codes of the algorithm would be great.
Discuss the advantages or limitations of your algorithm.
2 k-mean vs CL
Compare the k-mean algorithm with competitive learning (CL) algorithm. Could you apply the idea of Rival Penalized Competitive Learning (RPCL) to k-mean so that the number of clusters is automatically determined? If so, give the details of your algorithm and then implement it on a three-cluster dataset generated by yourself. If not, state the reasons.
3 model selection of GMM
Write a report on experimental comparisons on model selection performance between BIC,AIC and VBEM.
Specifically, you need to randomly generate datasets based on GMM, by varying some factors, e.g., sample sizes, dimensionality, number of clusters, and so on.
• BIC, AIC: First, run EM algorithm on each dataset X for k = 1,…,K, and calculate the log-likelihood value ln[p(X|Θˆk)], where Θˆk is the maximum likelihood estimate for parameters; Second, select the optimal k∗ by
k∗ = argmaxk=1,…,KJ(k), (1)
JAIC(k) = ln[p(X|Θˆk)] − dm (2)
∗ [email protected]
(3)
• Use VBEM algorithm for GMM to select the optimal k∗ automatically or via evaluating the lower bound.
The following codes might be useful.
Matlab: http://www.cs.ubc.ca/~murphyk/Software/VBEMGMM/index.html Python: http://scikit-learn.org/stable/modules/mixture.html
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