3 accepted papers
In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels capturing multi-feature associations. Then, based on Riemannian geometry, a composite kernel is computed, extracting the diff…
The need for efficiently comparing and representing datasets with unknown alignment spans various fields, from model analysis and comparison in machine learning to trend discovery in collections of medical datasets. We use manifold learning to compare the intrinsic geometric structures of different…
In this paper we address the detection of Alzheimer's disease based solely on EEG recordings. We assume that the state of Alzheimer's disease can be described by a latent manifold, captured by the EEG sensors and apply alternating diffusion to reveal this common underlying manifold from multiple EEG…