|
|
|
|
RI | Publications | Multimodal Oriented Discriminant Analysis
|
|
Text only version of this site
Multimodal Oriented Discriminant Analysis
F. De la Torre Frade and T. Kanade
tech. report CMU-RI-TR-05-03, Robotics Institute, Carnegie Mellon University, January, 2005.
Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference
| Download [Help] |
Adobe portable document format (pdf) [196 KB]
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
| Abstract |
Linear discriminant analysis (LDA) has been an active topic of research during the last century. However, the existing algorithms have several limitations when applied to visual data. LDA is only optimal for Gaussian distributed classes with equal covariance matrices, and only classes-1 features can be extracted. On the other hand, LDA does not scale well to high dimensional data (over-fitting), and it cannot handle optimally multimodal distributions. In this paper, we introduce Multimodal Oriented Discriminant Analysis (MODA), an LDA extension which can overcome these drawbacks. A new formulation and several novelties are proposed:
1) An optimal dimensionality reduction for multimodal Gaussian classes with different covariances is derived. The new criteria allows for extracting more than classes-1 features. 2) A covariance approximation is introduced to improve generalization and avoid over-fitting when dealing with high dimensional data. 3) A linear time iterative majorization method is suggested in order to find a local optimum.
Several synthetic and real experiments on face recognition show that MODA outperform existing LDA techniques.
| Notes |
Associated center: VASC
Associated labs/groups: Human Identification at a Distance, Face Group, MultiRobot Lab, and People Image Analysis Consortium
Associated projects: Camera Assisted Meeting Event Observer and Component Analysis for Data Analysis
Number of pages: 22
| Text Reference |
F. De la Torre Frade and T. Kanade, Multimodal Oriented Discriminant Analysis, tech. report CMU-RI-TR-05-03, Robotics Institute, Carnegie Mellon University, January, 2005.
| BibTeX Reference |
@techreport{De la Torre Frade_2005_4906,
author = "Fernando De la Torre Frade and Takeo Kanade",
title = "Multimodal Oriented Discriminant Analysis",
institution = "Robotics Institute, Carnegie Mellon University",
month = "January",
year = "2005",
number = "CMU-RI-TR-05-03",
address = "Pittsburgh, PA"
}