Optimization on Lie manifolds and pattern recognition

Authors:
Nagabhushana Prabhu;Hung-Chieh Chang;Maria deGuzman
Affiliations:
School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA;School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA;School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
Venue:
Pattern Recognition
Year:
2005

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Abstract

Several pattern recognition problems can be reduced in a natural way to the problem of optimizing a nonlinear function over a Lie manifold. However, optimization on Lie manifolds involves, in general, a large number of nonlinear equality constraints and is hence one of the hardest optimization problems. We show that exploiting the special structure of Lie manifolds allows one to devise a method for optimizing on Lie manifolds in a computationally efficient manner. The new method relies on the differential geometry of Lie manifolds and the underlying connections between Lie groups and their associated Lie algebras. We describe an application of the new Lie group method to the problem of diagnosing malignancy in the cytological extracts of breast tumors. The diagnosis method that we present has a mean sensitivity of 98.086% and a predictive index of 0.0602, making it the most accurate and reliable diagnostic method reported thus far.