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 present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the late...
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,...
A novel multistage feedforward network is proposed for efficient solving of difficult classification tasks. The standard Radial Basis Functions (RBF) architecture is modified in or...