Research papers on GMM

Research papers on Gaussian Mixture Models (GMMs) explore how these statistical tools can identify and analyze complex data patterns. GMMs assume data is generated from multiple overlapping normal distributions (bell curves), allowing researchers to uncover hidden groupings or subpopulations within data. These models are widely used in fields like machine learning, pattern recognition, and data analysis because they provide a flexible way to model complex data structures, perform clustering, and estimate probabilities. Research often focuses on improving GMM algorithms, understanding their theoretical properties, or applying them to real-world problems such as image analysis, speech recognition, and customer segmentation.