Unsupervised Method

Unsupervised methods in machine learning aim to extract meaningful patterns and structures from data without relying on pre-labeled examples, addressing the limitations of supervised learning's dependence on extensive annotated datasets. Current research focuses on developing robust algorithms for clustering, anomaly detection, and representation learning, employing techniques like generative adversarial networks, spectral clustering in non-Euclidean spaces, and self-supervised learning with models such as Segment Anything Model (SAM). These advancements are significant for various applications, including early pandemic detection, fault diagnosis in engineering systems, and improving the efficiency and generalizability of machine learning models across diverse domains.