40 research outputs found
Estimation of Single-Gaussian and Gaussian mixture models for pattern recognition
Single-Gaussian and Gaussian-Mixture Models are utilized in various
pattern recognition tasks. The model parameters are estimated usually via
Maximum Likelihood Estimation (MLE) with respect to available training data.
However, if only small amount of training data is available, the resulting model
will not generalize well. Loosely speaking, classification performance given an
unseen test set may be poor. In this paper, we propose a novel estimation technique
of the model variances. Once the variances were estimated using MLE,
they are multiplied by a scaling factor, which reflects the amount of uncertainty
present in the limited sample set. The optimal value of the scaling factor is based
on the Kullback-Leibler criterion and on the assumption that the training and test
sets are sampled from the same source distribution. In addition, in the case of
GMM, the proper number of components can be determined