Unsupervised Feature Selection

Unsupervised feature selection aims to identify the most informative features from high-dimensional data without relying on labeled examples, improving the efficiency and effectiveness of subsequent analyses like clustering or dimensionality reduction. Recent research emphasizes methods leveraging matrix factorization, tensor decomposition, and graph-based approaches, often incorporating sparsity constraints and self-supervised learning techniques to enhance robustness and interpretability. These advancements are crucial for handling the challenges posed by large, complex datasets in various fields, leading to improved model performance and deeper insights from data analysis.