Spectral clustering is one of the most popular algorithms to group high- dimensional data. It easy implement and computationally efficient. Despite its popularity successful applications, theoretical properties have not been fully understood. In this paper, we show that spectral minimax optimal in Gaussian mixture model with isotropic covariance matrix, when number clusters fixed signal-to-nois...

Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative research methods, but non-linear probabilistic beyond limit linear model capacity. For example, variational autoencoders (VAEs) have been extensively CF, and achieved excellent results. Aiming at problem prior distribution for latent codes VAEs traditional CF t...

Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of variables. A clustering algorithm should be able, despite this heterogeneity, extract discriminant pieces information from variables in order design groups. In work we introduce a multilayer architecture model-based method called Mixed Deep Gaussian Mixture Model that can viewed as an automatic way m...

We propose an extension of the mixture of factor (or independent component) analyzers model to include strongly super-gaussian mixture source densities. This allows greater economy in representation of densities with (multiple) peaked modes or heavy tails than using several Gaussians to represent these features. We derive an EM algorithm to find the maximum likelihood estimate of the model, and...

The Data Availability Statement for this paper is incorrect. The correct Data Availability Statement is: Data are available at Figshare (http://figshare.com/articles/A_Fast_Incremental_ Gaussian_Mixture_Model/1552030). The MNIST data set is available at (http://yann.lecun. com/exdb/mnist/) and the CIFAR10 data set is available at (http://www.cs.toronto.edu/~kriz/ cifar.html). The software binar...

Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact, a new numer...

This paper describes a novel framework of voice conversion (VC). We call it eigenvoice conversion (EVC). We apply EVC to the conversion from a source speaker’s voice to arbitrary target speakers’ voices. Using multiple parallel data sets consisting of utterancepairs of the source and multiple pre-stored target speakers, a canonical eigenvoice GMM (EV-GMM) is trained in advance. That conversion ...

In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Mo...