Gaussian Mixture Model

Gaussian Mixture Models (GMMs) are probabilistic models used to represent data as a mixture of Gaussian distributions, aiming to identify underlying clusters or patterns within complex datasets. Current research focuses on improving GMM robustness and efficiency, particularly in high-dimensional spaces, through techniques like Expectation-Maximization (EM) algorithms, optimal transport methods, and integration with deep learning architectures such as neural networks and transformers. GMMs find broad application across diverse fields, including robotics (path planning, swarm control), audio processing (denoising, sound event detection), image processing (segmentation, registration), and financial modeling, demonstrating their versatility and impact on various scientific and engineering problems.

Papers

October 17, 2022

Overlap-guided Gaussian Mixture Models for Point Cloud Registration
Guofeng Mei, Fabio Poiesi, Cristiano Saltori, Jian Zhang, Elisa Ricci, Nicu Sebe
Point Cloud Registration Gaussian Mixture Model Gaussian Mixture Input Point Cloud Probabilistic Registration

October 5, 2022

GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
Chen Liang, Wenguan Wang, Jiaxu Miao, Yi Yang
Semantic Segmentation Segmentation Model Gaussian Mixture Model Dense Representation Generative Classifier

September 30, 2022

Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
Ye Tian, Haolei Weng, Yang Feng
Transfer Learning Multi Task Learning Multi Task Unsupervised Learning Gaussian Mixture Model Single Task Learning Mixture Training

September 29, 2022

Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Yubo Zhuang, Xiaohui Chen, Yun Yang
Gaussian Mixture Model Heterogeneous Data Expectation Maximization Semidefinite Programming Likelihood Based Model Based Clustering

September 27, 2022

LapGM: A Multisequence MR Bias Correction and Normalization Model
Luciano Vinas, Arash A. Amini, Jade Fischer, Atchar Sudhyadhom
Gaussian Mixture Model Magnetic Resonance Image Intrinsic Bias Unified Normalization

September 23, 2022

A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann
Variational Inference Gaussian Mixture Model Unified View Gradient Estimator Natural Gradient Variational Inference

September 19, 2022

SMIXS: Novel efficient algorithm for non-parametric mixture regression-based clustering
Peter Mlakar, Tapio Nummi, Polona Oblak, Jana Faganeli Pucer
Data Clustering Gaussian Mixture Model Novel Algorithm Longitudinal Analysis

September 16, 2022

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafal Wisniewski, Mads Græsbøll Christensen
Gaussian Mixture Model Expectation Maximization PRIvacy Leakage Decentralized Data Data Privacy Expectation Maximization Algorithm

August 24, 2022

Apple Counting using Convolutional Neural Networks
Nicolai Häni, Pravakar Roy, Volkan Isler
Convolutional Neural Network Gaussian Mixture Model Fruit Detection Fruit Counting

July 19, 2022

Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation
Darwin Saire, Adín Ramírez Rivera
Semantic Segmentation Deep Model World Event Gaussian Mixture Model Feature Representation Local Feature Semantic Image Segmentation Dense Training

July 11, 2022

Optimal Clustering by Lloyd Algorithm for Low-Rank Mixture Model
Zhongyuan Lyu, Dong Xia
Gaussian Mixture Model Mixture Model Optimal Clustering Lloyd Algorithm Matrix Variate

July 7, 2022

Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data
Hongkang Li, Shuai Zhang, Meng Wang
Neural Network LeArning Abstract Strong Generalization Generalization Error Gaussian Mixture Model Learning Problem Gaussian Data

June 28, 2022

Gaussian Latent Dirichlet Allocation for Discrete Human State Discovery
Congyu Wu, Aaron Fisher, David Schnyer
Gaussian Mixture Model Latent Dirichlet Allocation Unsupervised Probabilistic Model Psychophysiological Measure

June 25, 2022

On how to avoid exacerbating spurious correlations when models are overparameterized
Tina Behnia, Ke Wang, Christos Thrampoulidis
Full Model Cross Entropy Loss Gaussian Mixture Model Spurious Correlation Data Imbalance Generalization Error Bound

June 19, 2022

GMM based multi-stage Wiener filtering for low SNR speech enhancement
Wageesha Manamperi, Prasanga N. Samarasinghe, Thushara D. Abhayapala, Jihui Zhang
Speech Enhancement Gaussian Mixture Model Non Stationary Single Channel Speech Enhancement Noise Model Wiener Filter

June 17, 2022

On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models
Pascal Mattia Esser, Frank Nielsen
External Influence Gaussian Mixture Model Learning Dynamic Partial Identifiability Model Space Singular Learning

June 7, 2022

June 6, 2022

Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
Yihan Zhang, Nir Weinberger
High Dimensional Gaussian Mixture Model Mean Estimation Gaussian Model Markov Model Dimensional Gaussian Mixture Homogeneous Markov

June 4, 2022

Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images
Ahmad Chaddad, Lama Hassan, Christian Desrosiers
Covid 19 Random Forest Gaussian Mixture Model Radiomics Feature X Ray Image GMM CNN