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Questions tagged [gmm]

For questions about Gaussian mixture models (GMMs).

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Why posterior in EM algorithm tractable but in VAE not?

So I've read through this post, including the Bayesian Mixture of Gaussians supplied in the link in the last comment by oW_ there, to really see why the posterior is intractable. I just don't see yet ...
Anon's user avatar
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Two overlap gaussian mixtures come from one or two generations of data?

Reviewing for a ML exam, my teacher asked last year a philosophical question about gaussians and generations of data which I couldn’t find no good answer. Imagine that an oracle can say with 100% ...
Adrian Lara's user avatar
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scikit GMM fails randomly with ill-defined linear covariances

I am trying to fit a GMM model to my data. The dataset has 37 features (some int and others float). When the dataset has small number of rows (<200), GMM fits OK. When I try a larger dataset (500 ...
RedBaron's user avatar
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1 answer
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Method to Compare the Fit of of k-Medoids and GMM to my Dataset

I'm looking for a method(s) to compare the fit of k-Medoids and a GMM. Currently, I'm looking at the distance between the max-min means of the GMM clusters and comparing that to the max-min medoid ...
Andrew Feenan's user avatar
3 votes
1 answer
410 views

Math of Gaussian Mixture Models & EM Algorithm

I'm having a hard time understanding the math behind GMMs. A GMM is a weighted sum over K different Gaussian components with parameters $\mu_k, \sigma_k, \pi_k$ From my understanding, the general ...
bosonphoton's user avatar
1 vote
1 answer
347 views

Treating Word Embeddings as Multivariate Gaussian Random Variables

I want to specify some probabilistic clustering model (such as a mixture model or lda) over words, and instead of using the traditional method of representing words as an indicator vector , I want to ...
ricardo's user avatar
  • 23
3 votes
1 answer
175 views

Why Gaussian mixture model uses Expectation maximization instead of Gradient descent?

Why Gaussian mixture model uses Expectation maximization instead of Gradient descent? What other models uses Expectation maximization to find best optimal parameters instead of using gradient descent?
star's user avatar
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1 vote
1 answer
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Gaussian Mixture Models Clustering

When using the EM algorithm in Gaussian Mixture Models (GMM), in the E-step, we take each x set in the training dataset to calculate and update the "weight" and parameters of each Gaussian ...
YCCCCC's user avatar
  • 131
2 votes
1 answer
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Which algorithm or tool to use to classify as good or bad?

I have a feature vector with different data types, Considering all the data in that feature vector. I have to classify as Good or Bad. Which algorithm should be used to just get the output Good or ...
Amruth's user avatar
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