An in-depth description of an apparently forgotten matrix operation, the reversal operator, is developed. The properties of such an operation are also given, resulting in a new vector-matrix operation ...

The Data Science Lab Matrix Inverse from Scratch Using SVD Decomposition with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on an implementation of the ...

We sought to leverage machine learning to recognize and generalize patterns in 3D tensors, then use the trained ‘agent’ to find efficient decompositions of the matrix-multiplication tensor.

To learn incomplete tensors, tensors with constraints, or to add other loss terms, tntorch can be utilized. Tensors can be accessed in tntorch using various methods, including basic indexing, fancy ...

NTFk performs a novel unsupervised Machine Learning (ML) method based on Tensor Decomposition coupled with sparsity and nonnegativity constraints. NTFk has been applied to extract the temporal and ...

A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM Journal on Imaging Sciences, 6 (3), 1758-1789.

Many spectral unmixing approaches ranging from geometry, algebra to statistics have been proposed, in which nonnegative matrix factorization (NMF)-based ones form an important family. The original NMF ...

Many spectral unmixing approaches ranging from geometry, algebra to statistics have been proposed, in which nonnegative matrix factorization (NMF)-based ones form an important family. The original NMF ...