Sklearn GaussianMixture

Question

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 ?

Answer 1

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