Representation Matrix

Representation matrices are fundamental tools in machine learning and signal processing, aiming to capture essential information from data in a compact and computationally efficient form. Current research focuses on improving the accuracy and efficiency of these matrices within various contexts, including subspace clustering (using techniques like ADMM unfolding and functional link neural networks), independent component analysis, and continual learning (leveraging low-rank feature representations). These advancements enhance the performance of diverse applications, such as hyperspectral image analysis, EEG signal processing, and improving the stability and plasticity of deep learning models for continual learning. The development of more robust and interpretable representation matrices is crucial for advancing numerous fields, leading to better algorithms and more insightful data analysis.