Unsupervised Learning

Unsupervised learning aims to extract meaningful patterns and structures from unlabeled data, addressing the limitations of supervised methods that require extensive labeled datasets. Current research focuses on developing robust algorithms and model architectures, such as autoencoders, generative models (like Gaussian Mixture Models), and contrastive learning approaches, to improve clustering, anomaly detection, and representation learning. These advancements are impacting diverse fields, including medical image analysis, financial market prediction, and signal processing, by enabling efficient analysis of large, unlabeled datasets and reducing reliance on expensive manual labeling.