Spectral clustering based null space linear discriminant analysis (SNLDA)

Authors:
Wenxin Yang;Junping Zhang
Affiliations:
Department of Electronic Engineering, Fudan University, Shanghai, China;Shanghai Key Lab. of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, Shanghai, China and State Key Laboratory of Rail Traffic Control and Safe ...
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

Citing 3
Cited 0

The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Spectral Clustering

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Subclass Discriminant Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

While null space based linear discriminant analysis (NLDA) obtains a good discriminant performance, the ability easily suffers from an implicit assumption of Gaussian model with same covariance each class. Meanwhile, mixture model discriminant analysis, which is a good way for processing issues on multiple subclasses in each class, depends on human experience on the number of subclasses and has a highly complex iterative process. Considering the cons and pros of the two mentioned approaches, we therefore propose a new algorithm, called Spectral clustering based Null space Linear Discriminant Analysis (SNLDA). The main contributions of the algorithm include the following three aspects: 1) Employing a new spectral clustering method which can automatically detect the number of clusters in each class. 2) Finding a unified null space for processing multi-subclasses issues with eigen-solution technique. 3) Refining the calculation of the covariance matrix in a single sample subclass. The experimental results show the promising of the proposed SNLDA algorithm.