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AssigneeJohnson & Johnson Pharmaceutical Reseach & Develpment, L.L.C.
InventorsDimitris K Agrafiotis, Victor S Lobanov, Francis R Salemme
Estimating the term…
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.
A computer method of mapping one or more combinatorial libraries large multi-dimensional data sets from an N-dimensional space to an M-dimensional space, wherein N is greater than M, comprising:
A computer method of representing relationships for one or more combinatorial libraries between M output objects as distances from one another on a display map, comprising:
A system of representing relationships for one or more combinatorial libraries between M output objects from a set of N input objects as distances from one another on a display map, comprising:
Dependencies are inferred from the claim text.