Supervised learning of Gaussian mixture models for visual vocabulary generation

Supervised learning of Gaussian mixture models for visual vocabulary generation

The creation of semantically relevant clusters is vital in bag-of-visual words models which are known to be very successful to achieve image classification tasks. Generally, unsupervised clustering algorithms, such as K-means, are employed to create such clusters from which visual dictionaries are deduced. K-means achieves a hard assignment by associating each image descriptor to the cluster wi...

Subspace Gaussian Mixture Models for Large Vocabulary Speech Recognition

Subspace Gaussian mixture model(GMM) is an alternative approach to approximate the probabilistic density function (p.d.f) of a set of independent identical distributed (i.i.d) data with prior density estimates. In this approach, the prior density of GMM parameters is estimated from a development dataset, and when predict the new enrolled data, the prior knowledge can be utilised by criteria lik...

Boosting Mixture Models for Semi-supervised Learning

Semi-Supervised Learning of Mixture Models

This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this “degradation” phenomenon and show that it is due to the fact that bias may be adversely affected by unlabeled ...

On semi-supervised learning of Gaussian Mixture Models for phonetic classification

This paper investigates semi-supervised learning of Gaussian mixture models using an unified objective function taking both labeled and unlabeled data into account. Two methods are compared in this work – the hybrid discriminative/generative method and the purely generative method. They differ in the criterion type on labeled data; the hybrid method uses the class posterior probabilities and th...