Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition. (arXiv:2401.16836v1 [cs.LG])

Nonnegative Matrix Factorization (NMF) is an important unsupervised learning
method to extract meaningful features from data. To address the NMF problem
within a polynomial time framework, researchers have introduced a separability
assumption, which has recently evolved into the concept of coseparability. This
advancement offers a more efficient core representation for the original data.
However, in the real world, the data is more natural to be represented as a
multi-dimensional array, such as images or videos. The NMF's application to
high-dimensional …

advancement arxiv concept core cs.lg data extract factorization features framework matrix polynomial representation researchers tensor unsupervised unsupervised learning