Abstract: A method, system and computer program product are provided for scaling, or dimensionally reducing, multi-dimensional data sets that scale well for large data sets. The invention scales multi-dimensional data sets by determining one or more non-linear functions between a sample of points from the multi-dimensional data set and a corresponding set of dimensionally reduced points. Thereafter, these one or more non-linear functions are used to non-linearly map additional points. The additional points may be members of the original multi-dimensional data set or may be new, previously unseen points. In an embodiment, the determination of the non-linear relationship between the sample of points from the multi-dimensional data set and the corresponding set of dimensionally reduced points is performed by a self-learning system such as a neural network. The additional points are mapped using the self-learning system in a feed-forward/predictive manner.
Type:
Grant
Filed:
May 2, 2003
Date of Patent:
October 3, 2006
Assignee:
Johnson & Johnson Pharmaceutical Reseach & Develpment, L.L.C.
Inventors:
Dimitris K Agrafiotis, Victor S Lobanov, Francis R Salemme