Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospita...
Nicolas Anciaux, Mehdi Benzine, Luc Bouganim, Phil...
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept ...
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi ...
Distance-preserving projection based perturbation has gained much attention in privacy-preserving data mining in recent years since it mitigates the privacy/accuracy tradeoff by ac...
Background: Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much ...
Benjamin Chain, Helen Bowen, John Hammond, Wilfrie...