Rapid and brief communication: Laplacian linear discriminant analysis

Authors:
Hong Tang;Tao Fang;Peng-Fei Shi
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, PR China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, PR China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, PR China
Venue:
Pattern Recognition
Year:
2006

Citing 3
Cited 4

KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communications: Kernel direct discriminant analysis and its theoretical foundation

Pattern Recognition

On an alternative formulation of the Fisher criterion that overcomes the small sample problem

Pattern Recognition
A comment on "Laplacian linear discriminant analysis"

Pattern Recognition
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the relevance of linear discriminative features

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

A novel linear discriminant criterion function is proved to be equal to Fisher's criterion function. The analysis of the function is linked to spectral decomposition of the Laplacian of a graph. Moreover, the function is maximized using two algorithms. Experimental results show the effectiveness and some specific characteristics of our algorithms.