Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images
Xianlu Li Nicolas Nadisic Shaoguang Huang Aleksandra Pižurica
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 widely
used in inverse imaging problems, such as image restoration, reconstruction,
and super-resolution, but has not been sufficiently explored yet in the context
of clustering. In this work, we introduce an innovative clustering architecture
for hyperspectral images (HSI) by unfolding an iterative solver based on the
Alternating Direction Method of Multipliers (ADMM) for sparse subspace
clustering. To our knowledge, this is the first attempt to apply unfolding ADMM
for computing the self-representation matrix in subspace clustering. Moreover,
our approach captures well the structural characteristics of HSI data by
employing the K nearest neighbors algorithm as part of a structure preservation
module. Experimental evaluation of three established HSI datasets shows clearly
the potential of the unfolding approach in HSI clustering and even demonstrates
superior performance compared to state-of-the-art techniques.