PCA reduced Gaussian mixture models with applications in superresolution

Discriminative Feature Extraction based on PCA Gaussian Mixture Models

A discriminative feature extraction based on Principal component analysis (PCA) and Gaussian mixture models is presented to increase the discrimative capability of modified two dimentional root cepstrum analysis (MTDRC). The exprimental results show that F-ratio tests indicate better separability of phonemes by using discriminative feature extraction than MTDRC. Key–Words: Feature extraction,PC...

Multiresolution Gaussian Mixture Models: Theory and Applications

Visualisation of Reduced-Dimension Microarray Data Using Gaussian Mixture Models

Dimensionality reduction, clustering and visualisation methods proposed in recent years have afforded new possibilities for the analysis of gene expression data. However, efficient, novel techniques for processing and representing microarray data are still required. We propose the use of the discrete cosine and sine transformations for dimensionality reduction of microarray data. These techniqu...

Fuzzy Gaussian Mixture Models

In this paper, in order to improve both the performance and the efficiency of the conventional Gaussian Mixture Models (GMMs), generalized GMMs are firstly introduced by integrating the conventional GMMs and the active curve axis GMMs for fitting non-linear datasets, and then two types of Fuzzy Gaussian Mixture Models (FGMMs) with a faster convergence process are proposed based on the generaliz...

Parsimonious Gaussian mixture models

Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases. In particular, a class of eight parsimonious Gaussian mixture models which ...