arXiv research

Enhances mixture models with classifier-defined weights.

Optimal mixtures of generative models outperform individual models on image datasets.

New bounds on sample size for identifying mixture models with grouped samples.

We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable corresponding to the mixture components is hidden and each mixture component over the observed variables can have a potentially different Markov graph structure and parameters. We propose a novel approach for estimati…

Spatially constrained Gaussian mixture models reduce covariance complexity.

When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same gro…

Improved VB algorithm for NIG mixtures outperforms Gaussian mixtures for non-Gaussian data.

The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…

The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametr…

Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.

A new method for fast Bayesian mixture model estimation.

The paper uses Gaussian mixture models for Bayesian networks and proposes an optimization algorithm.

A new method detects outliers using ensembles of Dirichlet process mixtures.

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, wh…

Paper uses Gaussian mixture models and Wasserstein distance for schema matching.

Bayesian approach learns nonparametric mixture components from heterogeneous data.

SMT trains generative models by estimating mixture scores, outperforming existing methods.

New method estimates mixture model components efficiently.

New method for summarizing Bayesian mixture models using sliced Wasserstein distances.

Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating …

Algorithm estimates nonparametric mixtures from grouped data.

Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the Expectation Maximization (EM) algorithm, in which updates are easy to derive for a wide class of mixture models. However, the likelihood of…

The paper develops methods to create reliable prediction sets for complex mixture models in high-dimensional data.

A new method uses Mean Field Games to optimize mixture models of Bernoulli and categorical distributions.

The paper tackles learning mixtures of two multinomial logits, showing identifiability and presenting an algorithm.

Study uniform rates for estimating Gaussian mixtures without separation assumption.

The study bounds the stability of Gaussian mixtures under small perturbations.

Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations. The current practice for estimating the parameters of such models relies on local search heuristics (e.g., the EM algorithm) which are prone to failure, and existing consistent methods …

Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.

Audio source separation is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals). Deep learning models are the state-of-the-art in source separation, given that the mixture to be separated is similar to the mixtures the deep model was tr…

A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mi…

New method reduces mixture model evaluation cost for large models.

We present the multidimensional membership mixture (M3) models where every dimension of the membership represents an independent mixture model and each data point is generated from the selected mixture components jointly. This is helpful when the data has a certain shared structure. For example, three unique means and …

Improves sequence modeling with a flow-based recurrent mixture density network.

We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the great flexibility offered by the copulas framework to extend mixtures of Canonical Correlation Analysis to multivariate data with arbitrary c…

We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that …

New concept of mixture complexity helps detect gradual clustering changes.

Heavy-tailed distributions are widely used in robust mixture modelling due to possessing thick tails. As a computationally tractable subclass of the stable distributions, sub-Gaussian $α$-stable distribution received much interest in the literature. Here, we introduce a type of expectation maximization algorithm that e…

Optimal transport for vector Gaussian mixtures improves efficiency and structure preservation.

Efficient algorithms for sparse parameter recovery in mixture models.

Paper detects gradual changes in cluster structure using MC fusion.

Improves scalability and efficiency of mixture models in black-box variational inference.

Proposes a new model for clustering with heavier tails.

In this paper we propose a new class of Dynamic Mixture Models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information coming from the data. The information driven nature of the proposed class of models allows to exactly compute the full likelihood and to avo…

Simplified explanation of ML for mixtures and OT.

We introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a Gaussian mixture model given one data sample per mixture component. Instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of eff…

New method uses dendrograms for better mixture model selection and clustering.

Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification. When dealing with mixture models, there are many issues that the experimenter should be aware of and needs to solve. The MixEst toolbox is a powerful and user-friendly package for M…