Nonnegative Tensor Factorization

Nonnegative Tensor Factorization (NTF) is a technique used to decompose multi-dimensional data (tensors) into meaningful, non-negative components, aiming to extract underlying features and patterns. Current research emphasizes developing more efficient and robust NTF algorithms, including those incorporating regularization techniques (e.g., attractor-based or manifold regularization) to improve model interpretability and handle complex data structures. Applications span diverse fields, from audio processing and image analysis to urban planning, where NTF's ability to analyze high-dimensional data with preserved spatial correlations offers significant advantages over traditional methods. The development of novel algorithms, such as those leveraging coseparability and Wasserstein distance, continues to improve the accuracy and efficiency of NTF for various applications.