Penalized Fisher discriminant analysis and its application to image-based morphometry

Authors:
Wei Wang;Yilin Mo;John A. Ozolek;Gustavo K. Rohde
Affiliations:
Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States;Department of Pathology, Children's Hospital of Pittsburgh, Pittsburgh, PA 15224, United States;Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States and Department of Electrical and Computer Engineering, Carneg ...
Venue:
Pattern Recognition Letters
Year:
2011

Citing 8
Cited 1

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Active shape models—their training and application

Computer Vision and Image Understanding
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational anatomy: an emerging discipline

Quarterly of Applied Mathematics - Special issue on current and future challenges in the applications of mathematics
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
A graph-based method for detecting characteristic phenotypes from biomedical images

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Regularized Discriminant Analysis, Ridge Regression and Beyond

The Journal of Machine Learning Research

A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

International Journal of Computer Vision

Quantified Score

Hi-index	0.10

Visualization

Abstract

Image-based morphometry is an important area of pattern recognition research, with numerous applications in science and technology (including biology and medicine). Fisher linear discriminant analysis (FLDA) techniques are often employed to elucidate and visualize important information that discriminates between two or more populations. We demonstrate that the direct application of FLDA can lead to undesirable errors in characterizing such information and that the reason for such errors is not necessarily the ill conditioning in the resulting generalized eigenvalue problem, as usually assumed. We show that the regularized eigenvalue decomposition often used is related to solving a modified FLDA criterion that includes a least-squares-type representation penalty, and derive the relationship explicitly. We demonstrate the concepts by applying this modified technique to several problems in image-based morphometry, and build discriminant representative models for different data sets.