Tensor decomposition and applications

Über Tensor decomposition and applications

This cluster of papers focuses on the theory and applications of tensor decompositions, particularly in the context of multilinear algebra. It covers various decomposition methods such as Singular Value Decomposition, Parallel Factor Analysis, Canonical Polyadic Decomposition, and Tucker Decomposition, along with their applications in signal processing and machine learning.

Schlagwörter

Tensor Decomposition Multilinear Algebra Singular Value Decomposition Parallel Factor Analysis Canonical Polyadic Decomposition Tucker Decomposition Nonnegative Tensor Factorization Higher-Order Tensors Signal Processing Machine Learning

Entwicklung im Zeitverlauf