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Penalized Fisher Discriminant Analysis and Its Application to Image-Based Morphometry.
Wei Wang1, Yilin Mo, John A Ozolek
1Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA. 15213.
Pattern Recognition Letters
|December 6, 2011
Summary
Direct Fisher Linear Discriminant Analysis (FLDA) can cause errors in image-based morphometry. A modified FLDA approach, incorporating a penalty term, improves population discrimination and model building for scientific data.
Area of Science:
- Pattern recognition
- Image analysis
- Biometrics
Background:
- Image-based morphometry utilizes pattern recognition for biological and medical applications.
- Fisher Linear Discriminant Analysis (FLDA) is commonly used for population discrimination.
Purpose of the Study:
- To identify errors in direct FLDA application for image-based morphometry.
- To propose and validate a modified FLDA technique for improved population characterization.
Main Methods:
- Investigated errors in direct FLDA, challenging assumptions about eigenvalue problems.
- Derived the relationship between regularized eigenvalue decomposition and a modified FLDA criterion with a penalty term.
- Applied the modified FLDA to image-based morphometry datasets.
Main Results:
- Direct FLDA can lead to undesirable characterization errors.
- The modified FLDA technique, incorporating a least-squares-type penalty, addresses these errors.
- Discriminant representative models were successfully built for various datasets.
Conclusions:
- The modified FLDA approach offers a more robust method for population discrimination in image-based morphometry.
- This technique improves the accuracy of discriminant models derived from image data.
- The findings have implications for pattern recognition in biology and medicine.
