arXiv research

Estimates RL data for dynamic treatment effects using GMM.

DGMM improves Gaussian mixture modeling efficiency and stability.

The GMM (generalized min-max) kernel was recently proposed (Li, 2016) as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the GMM kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GCWS) to convert the GMM ker…

New algorithm for learning mixtures with mostly uniform weights, improving on previous bounds.

Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.

Unified perspective on natural gradient methods for GMMs, improving variational inference.

The method of "random Fourier features (RFF)" has become a popular tool for approximating the "radial basis function (RBF)" kernel. The variance of RFF is actually large. Interestingly, the variance can be substantially reduced by a simple normalization step as we theoretically demonstrate. We name the improved scheme …

The recently proposed "generalized min-max" (GMM) kernel can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in with linearized radial basis function (RBF) kernel. On a large number of classificatio…

GMM-HMMs improve malware classification compared to discrete HMMs.

An $\varepsilon$-coreset for a given set $D$ of $n$ points, is usually a small weighted set, such that querying the coreset \emph{provably} yields a $(1+\varepsilon)$-factor approximation to the original (full) dataset, for a given family of queries. Using existing techniques, coresets can be maintained for streaming, …

Paper tackles MSDA with GMMs and OT, improving over prior art.

While tree methods have been popular in practice, researchers and practitioners are also looking for simple algorithms which can reach similar accuracy of trees. In 2010, (Ping Li UAI'10) developed the method of "abc-robust-logitboost" and compared it with other supervised learning methods on datasets used by the deep …

GAT-GMM improves GANs' performance in learning Gaussian mixture models.

In order to cluster or partition data, we often use Expectation-and-Maximization (EM) or Variational approximation with a Gaussian Mixture Model (GMM), which is a parametric probability density function represented as a weighted sum of $\hat{K}$ Gaussian component densities. However, model selection to find underlying …

Many methods for machine learning rely on approximate inference from intractable probability distributions. Variational inference approximates such distributions by tractable models that can be subsequently used for approximate inference. Learning sufficiently accurate approximations requires a rich model family and ca…

Paper estimates GMMs with unknown covariances using sparse regularization.

We investigate a Gaussian mixture model (GMM) with component means constrained in a pre-selected subspace. Applications to classification and clustering are explored. An EM-type estimation algorithm is derived. We prove that the subspace containing the component means of a GMM with a common covariance matrix also conta…

The paper studies scaling limits of Wasserstein metrics on Gaussian mixture models.

Efficiently trains large GMMs with millions to billions of parameters.

Paper analyzes GMM for separable data with various parameter structures.

Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard methods such as 2SLS, GMM, and more recent variants are significantly impeded when the causal effects are complex, the instruments are high-dimen…

NE-GMM uses ES and GMM to improve uncertainty quantification.

We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM). While the compressive sensing is performed globally on the entire image as implemented in our lensless camera, a low-rank GMM is imposed on the local image patches. This low-rank GMM is derived via eigenvalue th…

A new 1-iteration GMM learning algorithm improves robustness and accuracy.

UAPCA projects uncertain data to low dimensions using GMMs.

Study improves GMM learning performance through multi-task and transfer learning.

Following the very recent line of work on the ``generalized min-max'' (GMM) kernel, this study proposes the ``generalized intersection'' (GInt) kernel and the related ``normalized generalized min-max'' (NGMM) kernel. In computer vision, the (histogram) intersection kernel has been popular, and the GInt kernel generaliz…

Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.

New algorithm selects GMM components robustly and efficiently.

Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters. However, EM guarantees only convergence to a stationary point of the log-likelihood function, which c…

Low-rank MPPCA improves importance sampling in high dimensions.

New GMM models fit high-dimensional data with fewer parameters.

Paper introduces a new distance measure for Gaussian Mixture Models.

Optimal classifiers derived from GMMs are approximated by deep neural networks.

Paper introduces new Gromov-type distances for comparing Gaussian mixture models.

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 n…

Regularized EM algorithm improves GMM clustering in low sample settings.

Develops a robust GMM estimator for outlier-tolerant inference.

This paper analyzes MFVBI for GMM using statistical mechanics.

Regularized linear regression improves binary classification performance, especially with ridge and $\ell_1$ regularization.

Paper proposes PCA-GMM for efficient superresolution of material images.

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time spot follows a Gaussian mixture distribution, a fact exploited to softly matc…

Automatically differentiable estimation for BLP model reduces bias in demand estimation.

A new method for learning Gaussian Mixture Models using gradient descent.

A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) in biometric recognition of voice has been carried out and presented. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this res…

SLIM efficiently solves overidentified models in a scalable manner.

This paper finds the noise threshold for learning Gaussian mixture models equals channel capacity.

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximised. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, wh…