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.
Papers
Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques
Mikayla Calitis
Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis, Georgios Gakis, Ioannis G. Aviziotis, Martin Kathrein, Gabriele Pozzetti, Christoph Czettl, Stéphane P. A. Bordas, Andreas G. Boudouvis
Beyond the noise: intrinsic dimension estimation with optimal neighbourhood identification
Antonio Di Noia, Iuri Macocco, Aldo Glielmo, Alessandro Laio, Antonietta Mira