In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in hig...
We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian ei...
Anders Brun, Hae-Jeong Park, Hans Knutsson, Carl-F...
Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti ...
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or partbased representation of o...
We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show ...
Stefan Harmeling, Guido Dornhege, David M. J. Tax,...