Amaury Lendasse

TL;DR: This work presents a new mathematical formulation of the error function based on cosine similarity that provides a closed form equation for a change of error for exchanging assignments between two random samples (called a swap), and an extreme speed-up over the original method even for a very large corpus like the MNIST Handwritten Digits dataset.

TL;DR: Nonlinear projection methods are shown to be equivalent to the linear Principal Component Analysis when the prediction tool used on the new variables is linear.

TL;DR: Multiscale modeling and simulation of a molecule chain and an aluminum crystalline solid are presented as the applications of the proposed machine-learning-enhanced hierarchical multiscale method.

TL;DR: This work compares ELM and NNK both as part of a kernel method and in neural network context, and advises model selection also on the variance of ELM hidden layer weights, and not only on the number of hidden units.

TL;DR: A fast manifold learning strategy to estimate the underlying geometrical distribution and develop the relevant mathematical criterion on the basis of the extreme learning machine (ELM) in the high-dimensional space is proposed.