Sklearn GaussianMixture

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I have been learning for myself for several months artificial intelligence through a project of character recognition and transcription of handwriting. Until now I have successfully used Keras, Theano and Tensorflow by implementing CNN, CTC neural networks.

Today, I try to use Gaussian mixture models, the first step towards hidden markov models with Gaussian emission. To do so, I used the sklearn mixture with pca reduction to select the best model with Akaike and Bayesian information criterion. With type of covariance Full for Aic which provides a nice U-curve and Tied for Bic, because with Full covariance Bic gives just a linear curve. With 12.000 samples, I get the best model at 60 n-components for Aic and 120 n-components for Bic.

My input images have 64 pixels aside which represent only the capital letters of the English alphabet, 26 categories numbered from 0 to 25.

The fit method of Sklearn GaussianMixture ignore labels and the predict method returns the position of the component (0 to 59 or 0 to 119) into the n-components regarding the probabilities.

How to retrieve the original label the position of the character in a list using sklearn GaussianMixture ?

Oct 30, 2018 in Data Analytics by james
• 120 points

recategorized Oct 30, 2018 by Vardhan 74 views

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The GaussianMixture object implements the expectation-maximization (EM) algorithm for fitting mixture-of-Gaussian models. It can also draw confidence ellipsoids for multivariate models, and compute the Bayesian Information Criterion to assess the number of clusters in the data. A GaussianMixture.fit method is provided that learns a Gaussian Mixture Model from train data.

You can use GaussianMixture.fit for the retrieving the orignal label

For more details you can go through the reference

answered Oct 30, 2018 by Priyaj
• 56,120 points

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