Unsupervised Feature Learning

Unsupervised feature learning aims to automatically discover meaningful representations from unlabeled data, enabling tasks like clustering, dimensionality reduction, and improved downstream performance in various applications. Current research emphasizes developing robust algorithms, such as those based on nonnegative matrix factorization (NMF), contrastive learning, and diffusion-based methods, often incorporating graph regularization or hyperbolic space to enhance feature extraction. These advancements are significant because they reduce reliance on expensive labeled datasets, improving efficiency and applicability across diverse domains including image analysis, time series prediction, and multimodal emotion recognition.