Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images

arXiv:2404.07112v1 Announce Type: new
Abstract: Deep subspace clustering methods are now prominent in clustering, typically using fully connected networks and a self-representation loss function. However, these methods often struggle with overfitting and lack interpretability. In this paper, we explore an alternative clustering approach based on deep unfolding. By unfolding iterative optimization methods into neural networks, this approach offers enhanced interpretability and reliability compared to data-driven deep learning methods, and greater adaptability and generalization than model-based approaches. Hence, unfolding has become …

abstract arxiv clustering cs.cv explore function however images interpretability iterative loss networks optimization overfitting paper representation struggle type