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Patent Title: Self-organization of pattern data with dimension reduction through learning of non-linear variance-constrained mapping

Assignee: Computer Associates International, Inc.
Patent Number: US5734796
Issue Date: 03-31-1998
Application Number:
File Date:09-29-1995

Abstract: The subject system provides a self-organized reduced-dimension remapping of pattern data. The system functions to a mapping from an original pattern space to a reduced-dimension space in an unsupervised nonlinear manner, but with a constraint that the overall variance in a representation of the data be conserved. This approach relates to but is different from both the Karhuren-Loeve (K-L) transform and auto-associative approaches which emphasize feature extraction, and also from the Advanced Reasoning Tool (ART) and feature mapping approaches which emphasize category formation based on similarity in the original representation. The subject system is highly efficient computationally. The reduced-dimension representation is suitably further simplified with ART or feature mapping techniques, as appropriate and as desired.


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