arXiv research

GMM-HMMs improve malware classification compared to discrete HMMs.

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

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

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…

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.

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

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

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…

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

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 selects GMM components robustly and efficiently.

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

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 …

Paper introduces a new distance measure for Gaussian Mixture Models.

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…

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…

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

This paper analyzes MFVBI for GMM using statistical mechanics.

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

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…

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

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 …

Regularized EM algorithm improves GMM clustering in low sample settings.

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

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

SLIM efficiently solves overidentified models in a scalable manner.

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

This work links SOMs and GMMs, providing a mathematical basis for their use.

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

DGMM improves Gaussian mixture modeling efficiency and stability.

This paper uses Gaussian mixtures to mimic interactions in large language models.

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

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…

Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the other hand, user models based on Gaussian mixture models - universal ba…

New slicing methods speed up Gaussian mixture Wasserstein distance computations.

VampPrior Mixture Model improves clustering in DLVMs.

Gradient EM converges globally for over-parameterized Gaussian mixtures.

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 position follows a Gaussian mixture distribution, a fact exploited to softly …

Develops a GMM method to estimate roughness in stochastic volatility models.

Clustering algorithms are a cornerstone of machine learning applications. Recently, a quantum algorithm for clustering based on the k-means algorithm has been proposed by Kerenidis, Landman, Luongo and Prakash. Based on their work, we propose a quantum expectation-maximization (EM) algorithm for Gaussian mixture models…

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…

This work develops efficient methods for computing moments of Gaussian mixtures.

Paper uses GMM and MAF for probabilistic classification, outperforming simpler models.

Private learning of Gaussian Mixture Models without boundedness assumptions.

Develops a robust GMM estimator for outlier-tolerant inference.

Corrected whitening restores orthogonality in high-dimensional spherical Gaussian mixtures.