Tensor Norm

Tensor norms are mathematical tools used to measure the "size" of tensors, multi-dimensional arrays extending matrices, and are crucial for solving problems involving incomplete or noisy tensor data. Current research focuses on developing efficient algorithms, often based on non-convex optimization or integer programming, to minimize these norms for tasks like tensor completion and multi-view clustering. These advancements aim to improve the accuracy and computational efficiency of tensor-based methods in various applications, including image processing, recommender systems, and machine learning. The development of robust and scalable tensor norm minimization techniques is driving progress in these fields.