Method for generating a low-dimensional representation of...

Image analysis – Pattern recognition – Classification

Reexamination Certificate

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C382S225000, C382S190000, C382S168000

Reexamination Certificate

active

07412098

ABSTRACT:
A method represents a class of objects. A set of samples for the objects in the class is acquired, there being one sample for each object, and each sample includes a plurality of data values representing characteristics of the object. The samples are grouped into subsets such that each subset intersects at least one other subset. For each subset, a low-dimensional parameterization is determined. Nullspaces of the low-dimensional parameterizations are averaged to obtain a matrix whose nullspace contains a low-dimensional representation of the class of objects.

REFERENCES:
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patent: 2004/0107079 (2004-06-01), MacAuslan
“A new LDA-based face recognition system which can solve the small sample size problem;” Chen et. al., pattern Recognition vol. 33, Issue 10; Oct. 2000, p. 1713-1726.
Tenenbaum, et al., “A global geometric framework for nonlinear dimensionality reduction,” Science, 290:2319-2323, Dec. 2000.
Weinberger, et al., “Learning a kernel matrix for nonlinear dimensionality reduction,” Proc. 21st ICML, 2004.
Brand, “Charting a manifold,” Advances in Neural Information Processing Systems, vol. 15, 2003.
Golub, et al., “Matrix Computations,” 3rdEdition, The Johns Hopkins University Press, 1996, p. 601.

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