Multilinear and nonlinear generalizations of partial least squares: an overview of recent advances

Partial least squares for IS researchers: an overview and presentation of recent advances using the PLS approach

Tensor Decompositions: A New Concept in Brain Data Analysis?

Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), Nonnegative Matrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover, tensor decompositions hav...

Superlinearly convergent exact penalty projected structured Hessian updating schemes for constrained nonlinear least squares: asymptotic analysis

We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...

Least Squares Methods for Models Including Ordinary and Partial Differential Equations

This contribution gives a brief overview on nonlinear constrained least squares methods. The focus is on data fitting problems in which the models include ordinary or partial differential equations.

Generalizations of the total least squares problem

Recent advances in total least squares approaches for solving various errorsin-variables modeling problems are reviewed, with emphasis on the following generalizations: 1. the use of weighted norms as a measure of the data perturbation size, capturing prior knowledge about uncertainty in the data; 2. the addition of constraints on the perturbation to preserve the structure of the data matrix, m...