Data processing: artificial intelligence – Neural network
Reexamination Certificate
1999-05-03
2003-05-27
Follansbee, John (Department: 2121)
Data processing: artificial intelligence
Neural network
C707S793000
Reexamination Certificate
active
06571227
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to scaling of multi-dimensional data sets and, more particularly, to non-linear mapping of a sample of points from a multi-dimensional data set, determining one or more non-linear functions for the mapped sample of points, and mapping additional points using the one or more non-linear functions, including mapping members of the original multi-dimensional data set and mapping new and previously unseen points.
2. Related Art
Conventional techniques for multi-dimensional scaling do not scale well for large multi-dimensional data sets.
What is needed is a method, system, and computer program product for multi-dimensional scaling, which is fast and efficient for large multi-dimensional data sets.
SUMMARY OF THE INVENTION
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. Any suitable non-linear mapping or multi-dimensional scaling technique can be employed. The process then trains a system (e.g., a neural network), using the corresponding sets of points. During, or at the conclusion of the training process, the system develops or determines a relationship between the two sets of points. In an embodiment, the relationship is in the form of one or more non-linear functions. The one or more non-linear functions are then implemented in a system. Thereafter, additional n-dimensional points are provided to the system, which maps the additional points using the one or more non-linear functions, which is much faster than using conventional multi-dimensional scaling techniques. In an embodiment, the determination of the non-linear relationship is performed by a self-learning system such as a neural network. The additional points are then be mapped using the self-learning system in a feed-forward manner.
REFERENCES:
patent: 4773099 (1988-09-01), Bokser
patent: 4811217 (1989-03-01), Tokizane et al.
patent: 4859736 (1989-08-01), Rink
patent: 4908773 (1990-03-01), Pantoliano et al.
patent: 4935875 (1990-06-01), Shah et al.
patent: 4939666 (1990-07-01), Hardman
patent: 5010175 (1991-04-01), Rutter et al.
patent: 5025388 (1991-06-01), Cramer, III et al.
patent: 5155801 (1992-10-01), Lincoln
patent: 5167009 (1992-11-01), Skeirik
patent: 5181259 (1993-01-01), Rorvig
patent: 5240680 (1993-08-01), Zuckerman et al.
patent: 5260882 (1993-11-01), Blanco et al.
patent: 5265030 (1993-11-01), Skolnick et al.
patent: 5270170 (1993-12-01), Schatz et al.
patent: 5288514 (1994-02-01), Ellman
patent: 5307287 (1994-04-01), Cramer, III et al.
patent: 5323471 (1994-06-01), Hayashi
patent: 5331573 (1994-07-01), Balaji et al.
patent: 5434796 (1995-07-01), Weininger
patent: 5436850 (1995-07-01), Eisenberg et al.
patent: 5442122 (1995-08-01), Noda et al.
patent: 5463564 (1995-10-01), Agrafiotis et al.
patent: 5499193 (1996-03-01), Sugawara et al.
patent: 5519635 (1996-05-01), Miyake et al.
patent: 5524065 (1996-06-01), Yagasaki
patent: 5526281 (1996-06-01), Chapman et al.
patent: 5545568 (1996-08-01), Ellman
patent: 5549974 (1996-08-01), Holmes
patent: 5553225 (1996-09-01), Perry
patent: 5565325 (1996-10-01), Blake
patent: 5574656 (1996-11-01), Agrafiotis et al.
patent: 5585277 (1996-12-01), Bowie et al.
patent: 5598510 (1997-01-01), Castelaz
patent: 5602755 (1997-02-01), Ashe et al.
patent: 5602938 (1997-02-01), Akiyama et al.
patent: 5612895 (1997-03-01), Balaji et al.
patent: 5634017 (1997-05-01), Mohanty et al.
patent: 5635598 (1997-06-01), Lebl et al.
patent: 5670326 (1997-09-01), Beutel
patent: 5679582 (1997-10-01), Bowie et al.
patent: 5684711 (1997-11-01), Agrafiotis et al.
patent: 5703792 (1997-12-01), Chapman
patent: 5712171 (1998-01-01), Zambias et al.
patent: 5712564 (1998-01-01), Hayosh
patent: 5734796 (1998-03-01), Pao
patent: 5736412 (1998-04-01), Zambias et al.
patent: 5740326 (1998-04-01), Boulet et al.
patent: 5789160 (1998-08-01), Eaton et al.
patent: 5807754 (1998-09-01), Zambias et al.
patent: 5811241 (1998-09-01), Goodfellow et al.
patent: 5832494 (1998-11-01), Egger et al.
patent: 5845225 (1998-12-01), Mosher
patent: 5858660 (1999-01-01), Eaton et al.
patent: 5861532 (1999-01-01), Brown et al.
patent: 5866334 (1999-02-01), Beutel
patent: 5901069 (1999-05-01), Agrafiotis et al.
patent: 5908960 (1999-06-01), Newlander
patent: 5933819 (1999-08-01), Skolnick et al.
patent: 5960443 (1999-09-01), Young et al.
patent: 5995938 (1999-11-01), Whaley
patent: 6014661 (2000-01-01), Ahlberg et al.
patent: 6026397 (2000-02-01), Sheppard
patent: 6037135 (2000-03-01), Kubo et al.
patent: 6049797 (2000-04-01), Guha et al.
patent: 6185506 (2001-02-01), Cramer et al.
patent: 0 355 266 (1993-06-01), None
patent: 0 355 628 (1993-11-01), None
patent: 0 770 876 (1997-05-01), None
patent: 0 818 744 (1998-01-01), None
patent: WO 91/19735 (1991-12-01), None
patent: WO 92/00091 (1992-01-01), None
patent: WO 93/20242 (1993-10-01), None
patent: WO 94/28504 (1994-12-01), None
patent: WO 95/01606 (1995-01-01), None
patent: WO 97/09342 (1997-03-01), None
patent: WO 97/20952 (1997-06-01), None
patent: WO 97/27559 (1997-07-01), None
patent: WO 98/20437 (1998-05-01), None
patent: WO 98/20459 (1998-05-01), None
Vicent W. Porto et al, Alternative Neural Network Training Methods, 1995, IEEE, pp. 16-22.*
Anil K. Jain et al, Artificial Neural Networks: A Tutorial, 1996, IEEE, pp. 31-44.*
Copy of International Search Report for PCT/US99/09963, 7 pages.
Hosenpud, J.D. et al, “The Effect of Transplant Center Volume on Cardiac Transplant Outcome,”The Journal of the American Medical Association, American Medical Society, vol. 271, No. 23, Jun. 1994, pp. 1844-1849.
“3DP gains drug research patent”, Chemistry in Britain, The Royal Society of Chemistry, vol. 32, No. 1, Jan. 1996, 2 pages.
“Accelerate the Discovery Cycle with Chem-X!”, Source and date of publication unclear, 2 pages.
Agrafiotis, D. K., et al., “Stochastic Algorithms for Maximizing Molecular Diversity”,Journal of Chemical Information and Computer Sciences, American Chemical Society, vol. 37, pp. 841-851, (1997).
Alsberg, B.K. et al., “Classification of pyrolysis mass spectra by fuzzy multivariate rule induction-comparison with regression, K-nearest neighbour, neural and decision-tree methods”,Analytical Chimica Acta, Elsevier Science B.V., vol. 348, No. 1-3, pp. 389-407, (Aug. 20, 1997).
Amzel, L.M., “Structure-based drug design,”Current Opinion in Biotechnology, Current Biology Publications, vol. 9, No. 4, Aug. 1998, pp. 366-369.
Andrea, T.A. et al., “Applications of Neural Networks in Quantitative Structure-Activity Relationships of Dihydrofolate Reductase Inhibitors”,Journal of Medicinal Chemistry, American Chemical Society, vol. 34, No. 9, pp. 2824-2836, (1991).
Aoyama, T. and Hiroshi Ichikawa, “Obtaining the Correlation Indices between Drug Activity and Structural Parameters Using a Neural Network”,Chemical&Pharmaceutical Bulletin, Japan Publications Trading Co. (U.S.A.) Inc., vol. 39, No. 2, pp. 372-378, (1991).
Baum, R.M., “Combinatorial Approaches Provide Fresh Leads for Medicinal Chemistry”,Chemical&Engineering News, American Chemical Society, Feb. 7, 1994, (pp. 20-26).
Bentley, J. L., “Multidimensional Binary Search Trees Used for Associative Searching”,Communications of the ACM, Association for Computing Machinery, Inc., vol. 18, No. 9, pp.
Agrafiotis Dimitris K.
Lobanov Victor S.
Salemme Francis R.
3-Dimensional Pharmaceuticals Inc.
Follansbee John
Hirl Joseph P.
Sterne Kessler Goldstein & Fox
LandOfFree
Method, system and computer program product for non-linear... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method, system and computer program product for non-linear..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method, system and computer program product for non-linear... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3086808