large Gaussian mixture models
Factoring variations in natural images with deep Gaussian mixture models
... generative models for unsupervised learning, with many applications in Image processing [1, 2], natural language processing [3, 4], vision [5] and audio ...Generative models can be seen as the swiss army ...
9
Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
... The F1-score values are defined on the interval (0, 1), and if they are near one they represent a better classification, while small values, near zero, represent a low classification quality. However, to evaluate the ...
12
Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
... on Gaussian mixture model and a forward feature selection strategy to reduce the dimension of the data to be ...the large volume of data during the learning step, updates rules from the forward ...
14
Adaptive process monitoring using principal component analysis and Gaussian Mixture Models
... very large number of modes and removing modes that tend to become singular, the GEM algorithm, on the other hand, starts with a single mode and then adds more modes into the mixture model one after the ...
226
Enhanced Expectation–Maximization Clustering through Gaussian Mixture Models
... are Gaussian appropriated; Instead of utilizing the mean it will provide better prohibitive supposition ...the Gaussian for that we utilize an efficient optimization approach named as Expectation– ...
5
Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
... local models. These local models typically require stationary kernels for a notion of “dis- tance” and ...locality, mixture-of-experts (MoE) models [8] have been applied to GP regression [14, ...
10
VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS AARON NICHIE
... quite large for the computations this was reduced to a small set of representative feature vectors (codebook) sufficient to adequately describe the extracted speech features of the ...
10
Enhanced load profiling for residential network customers
... 50 Gaussian Mixture Models (GMM) were learned using maximum likelihood EM; from these 50 the optimal number of mixtures was selected using BIC, the results of which are shown in figure ...single ...
8
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
... with a large range of datasets. Furthermore, robust- ness is required so that the performance of the model does not change drastically with small changes in its parameter values. Generally it is hard to specify ...
12
Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering
... Nowadays, the vast use of mobile communication in different environments with different background noises, asks for powerful and accurate noise reduction algorithms to ensure the quality of communicated voice and the ...
8
An Overview on Speaker Identification Technologies
... There are various techniques and method s for speaker recognition. Researches are going on this area from last four decades and continue to be an active area. Approaches have spanned from human aural and spectrogram ...
10
Hybrid generative-discriminative training of Gaussian mixture models
... very large in case of high-dimensional input spaces, we propose to use a diagonal plus low-rank structure for the covariance matrices to reduce the parameter space considerably while still allow- ing important ...
8
Speaker identification using distributed vector quantization and Gaussian mixture models
... training Gaussian mixture speaker models as a replacement for Expectation Maximization (EM) algorithm to reduce computational ...too large, it faces the time consuming ...
165
Gas turbine engine condition monitoring using Gaussian mixture and hidden Markov models
... short time scale. Given a higher sampling frequency it is ex- pected that much shorter time scale transients will be able to be detected. The CUSUM of the synthetic faults given in Ta- bles 1 and 2 indicate that the ...
16
Dependent Gaussian mixture models for source separation
... The development of this methodology is motivated by the need to bring an efficient solution to the separa- tion of components in the microwave radiation maps to be obtained by the satellite mission Planck which has the ...
11
Gaussian mixture models and semantic gating improve reconstructions from human brain activity
... extremely large set of images is prob- ably necessary to span the space of natural images, in which case labels are probably not ...the mixture components were estimated in an unsupervised set- ...
11
Training Gaussian Mixture Models at Scale via Coresets
... of Gaussian mixture models by exploiting a connection between statistical estimation and clustering problems in computational ...of mixture models for large data ...the ...
25
Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees
... As proposed in [13], figure 3 shows the speaking-aid system with a voice conversion technique for laryngectomees. First, a user attaches a sound source unit under the lower jaw and articulates sound source signals. A ...
6
Topics in unsupervised learning
... of models can be seen as a gener alisation of the mixtures of factor analysers and mixtures of principal components analysers models and contains them both as special ...of models grows linearly ...
174
HISTOGRAM BASED AUTOMATIC IMAGE SEGMENTATION USING WAVELETS FOR IMAGE ANALYSIS
... Model-Based image segmentation plays a dominant role in image analysis and image retrieval. To analyze the features of the image, model based segmentation algorithm will be more efficient compared to non-parametric ...
7