Subspace Prototype

Subspace prototype methods aim to represent data clusters as low-dimensional subspaces, facilitating efficient and robust data analysis tasks such as classification, clustering, and denoising. Current research focuses on developing algorithms that learn these subspace prototypes effectively, often employing techniques like self-expressive models, Grassmann manifold optimization, and neural network architectures (e.g., deep subspace clustering nets) to handle high-dimensional data and address challenges like class imbalance and noise. These advancements improve the performance and interpretability of various machine learning models across diverse applications, including image and text processing, molecular dynamics analysis, and medical imaging.