Tensor Based

Tensor-based methods are revolutionizing data analysis and machine learning by leveraging the multi-dimensional structure of data, aiming to improve efficiency, accuracy, and scalability in various applications. Current research focuses on developing novel tensor decompositions (e.g., CP, Tucker, Tensor Train) and incorporating them into deep learning architectures (e.g., tensor networks, tensor transformers) for tasks such as data recovery, clustering, and recommendation. These advancements are significantly impacting fields like computer vision, natural language processing, and signal processing, enabling more efficient and robust solutions for complex problems involving high-dimensional data.