We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Concurrent with recent theoretical interest in the problem of metric embedding, a growing body of research in the networking community has studied the distance matrix defined by n...
: We consider the problem of efficiently evaluating a large number of XPath expressions, especially in the case when they define subscriber profiles for filtering of XML documen...
Panu Silvasti, Seppo Sippu, Eljas Soisalon-Soinine...
Background: Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene s...
Bob J. A. Schijvenaars, Barend Mons, Marc Weeber, ...
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...