Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a med...

Background subtraction methods are widely exploited for moving object detection in many applications. A key issue to these methods is how to model and maintain the background correctly and efficiently. This paper describes a foreground detector used in our surveillance system characterized by multiple Gaussian statistics. Compared with the existing methods, our Gaussian mixture model (GMM) diff...

We consider recursive Bayesian filtering where the posterior is represented as a Gaussian mixture model (GMM), and the likelihood function as a sum of scaled Gaussians (SSG). In each iteration of filtering, the number of components increases. We propose an algorithm for simplifying a GMM into a reduced mixture model with fewer components, which is based on maximizing a variational lower bound o...

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic bloc...

Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, generator and discriminator. While GANs achieve great success in learning complex image, sound, text data, they perform suboptimally multimodal distribution-learning benchmarks such as Gaussian mixture models (GMMs). In this paper, we propose Adversarial Trainin...

In this paper we present a new scheme for Palmprint verification. The proposed method can be viewed as a combination of Gaussian Mixture Model (GMM) followed by Independent Component Analysis (ICA I and ICA II) applied directly on the pixels. This approach follows the path opened by previous works making use of GMM followed by Principal Component Analysis (PCA) and Linear Discriminate Analysis ...

This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers’ space into small subsets of speakers within a hierarchical tree structure. Du...

Abstract: Polymer processes often contain state variables whose distributions are multimodal; in addition, the models for these processes are often complex and nonlinear with uncertain parameters. This presents a challenge for Kalman-based state estimators such as the ensemble Kalman filter. We develop an estimator based on a Gaussian mixture model (GMM) coupled with the ensemble Kalman filter ...

Feature reduction is one kind of pattern recognition and decision making technique, which can be achieved by using Fuzzy Weighted Gaussian Mixture Model (FWGMM) based on the Gaussian Mixture Model. This model helps to find relevant features by using Fuzzy ordered weighted average, which leads to determine the similarity of the density mixture. The salient feature of this approach is to find the...

While deep neural networks (DNNs) have become the dominant acoustic model (AM) for speech recognition systems, they are still dependent on Gaussian mixture models (GMMs) for alignments both for supervised training and for context dependent (CD) tree building. Here we explore bootstrapping DNN AM training without GMM AMs and show that CD trees can be built with DNN alignments which are better ma...