CP Decomposition

CP decomposition is a powerful technique for analyzing high-dimensional data by representing it as a sum of rank-one tensors, aiming to extract underlying structure and relationships. Current research focuses on improving the scalability of CP decomposition algorithms, particularly for extremely large datasets, often leveraging parallel computing architectures like GPUs and employing compression techniques. Furthermore, research explores applications in diverse fields such as natural language processing (generating constrained sentences), gene analysis, and machine learning (improving polynomial neural networks), highlighting its broad utility. These advancements enhance the applicability of CP decomposition to increasingly complex real-world problems.