William S. Rayens

TL;DR: Partial least squares (PLS) was not originally designed as a tool for statistical discrimination as discussed by the authors, but applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role.

TL;DR: Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model, and spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

TL;DR: An attempt to reduce the number of iterations that PARAFAC spends in a swamp, a particular method of stabilization is employed and results are presented which suggest that the number iterations can often be greatly reduced.

TL;DR: New results are introduced, including a formal proof for the superiority of PLS over PCA in the two-group case, as well as new connections between PLS for discrimination and an extended class of P LS-like techniques known as “oriented PLS” (OrPLS).

TL;DR: A comprehensive statistical analysis relating age, motor performance, DA release, and iron content indicated that the best predictor of decreases in motor ability was iron accumulation in the striatum, suggesting that striatal iron levels may be a biomarker of motor dysfunction in aging.