Introduction Gaussian Mixture Models (GMMs) are a popular probabilistic model used for representing a mixture of several Gaussian distributions. GMMs are highly effective for modeling data that exhibits multiple underlying subpopulations, especially in unsupervised learning tasks such as clustering, density estimation, and anomaly detection. They are used to approximate complex, multi-modal distributions, making them a…
Introduction Metric learning is a subfield of machine learning that focuses on learning a distance function that quantifies the similarity or dissimilarity between data points. Unlike traditional machine learning models that typically use fixed, pre-defined metrics (such as Euclidean distance), metric learning aims to learn the best metric that captures the underlying structure of the…
Introduction Semi-supervised learning (SSL) is a machine learning paradigm that combines both labeled and unlabeled data to improve the learning process. In traditional supervised learning, models are trained on a fully labeled dataset, where each input comes with a corresponding output. However, obtaining labeled data is often expensive, time-consuming, and labor-intensive, especially in complex domains.…