US 6,571,227 B1Granted

Method, system and computer program product for non-linear mapping of multi-dimensional data

Assignee3-Dimensional Pharmaceuticals, Inc.

InventorsDimitris K. Agrafiotis, Victor S. Lobanov, Francis R. Salemme

Life of this patent

Estimating the term…

Loading patent…

Abstract

A method, system and computer program product for scaling, or dimensionally reducing, multi-dimensional data sets, that scales 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, and thereafter using the non-linear function 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 invention begins with a sample of points from an n-dimensional data set and a corresponding set of m-dimensional points. Alternatively, the invention selects a sample of points from an n-dimensional data set and non-linearly maps the sample of points to obtain the corresponding set of m-dimensional points.

Claims34

Dependent claims

A scalable computer method for mapping large multi-dimensional data sets, comprising the steps of:

(a) placing a sample of points from a multi-dimensional data set on a display map, each point in the sample of points having an associated set of N input coordinates and an associated set of M output coordinates, wherein N is greater than M;
(b) selecting a sub-set of points from the sample of points, wherein the selected sub-set of points includes associated relationships between points in the selected sub-set;
(c) revising the associated set of M output coordinates for at least one of the points of the sub-set of points based on the relationships between the points;
(d) repeating steps (b) and (c) for additional sub-sets of points from the set of points;
(e) determining at least one non-linear function for the non-linearly mapped sample of points that maps for each point in the sample of points the associated set of N input coordinates to the associated set of M output coordinates produced in steps (a) through (d);
and
(f) mapping additional points from the multi-dimensional data set using the at least one non-linear function determined in step (e), whereby the mapping generates M-dimensional output coordinates.

4 more elements

A scalable computer method for mapping large multi-dimensional data sets, comprising the steps of:

(a) placing a sample of points from a multi-dimensional data set on a display map, each point in the sample of points having an associated set of N input coordinates and an associated set of M output coordinates, wherein N is greater than M;
(b) selecting a sub-set of points from the sample of points, wherein the selected sub-set of points includes associated relationships between points in the selected sub-set;
(c) revising the associated set of M output coordinates for at least one of the points of the sub-set of points based on the relationships between the points;
(d) repeating steps (b) and (c) for additional sub-sets of points from the set of points;
(e) selecting a second sub-set of points from the sample of points;
(f) determining a first non-linear function for the second sub-set of points that maps for each point in the second sub-set of points the associated set of N input coordinates to the associated set of M output coordinates produced in steps (a) through (d);
(g) selecting a third sub-set of points from the sample of points;
(h) determining a second non-linear function for the third sub-set of points that maps for each point in the third sub-set of points the associated set of N input coordinates to the associated set of M output coordinates produced in steps (a) through (d);
and
(i) mapping additional points from the multi-dimensional data set using the first non-linear function or the second non-linear function, whereby the mapping generates M-dimensional output coordinates.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 09/303,671Filed 3 May 1999 · continuation in part
Priority: 4 Nov 1996
Granted: 27 May 2003
Term ends: Not estimatedThe source records don’t support an estimate.

Classification3 codes

G06F 17/175
Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method: of multidimensional data
G06F 18/2137
Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods: based on criteria of topology…
G06F
Computing and data processing

Family

Application60/130,187Us provisional application · filed 4 Nov 1996
Application08/963,872Parent of this continuation-in-part · filed 11 Nov 1997
Application09/073,845Parent of this continuation-in-part · filed 7 May 1998
US 6,453,246 B1Parent of this continuation-in-part
US 6,295,514 B1Parent of this continuation-in-part
US 6,571,227 B1This patent

From the US filing history. Foreign family members aren’t listed.