A novel supervised dimensionality reduction algorithm: Graph-based Fisher analysis

Authors:
Yan Cui;Liya Fan
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China and School of Mathematics Sciences, Liaocheng University, Liaocheng 252059, China;School of Mathematics Sciences, Liaocheng University, Liaocheng 252059, China
Venue:
Pattern Recognition
Year:
2012

Citing 15
Cited 2

A Generalized Representer Theorem

COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
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
Feature Space Interpretation of SVMs with Indefinite Kernels

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

Pattern Recognition Letters
Local Fisher discriminant analysis for supervised dimensionality reduction

ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis

The Journal of Machine Learning Research
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving discriminant analysis for single training image face recognition

Pattern Recognition Letters
Graph-optimized locality preserving projections

Pattern Recognition
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing discriminant analysis using the generalized singular value decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

Pattern Recognition
Global plus local: A complete framework for feature extraction and recognition

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, a novel supervised dimensionality reduction (DR) algorithm called graph- based Fisher analysis (GbFA) is proposed. More specifically, we redefine the intrinsic and penalty graph and trade off the importance degrees of the same-class points to the intrinsic graph and the importance degrees of the not-same-class points to the penalty graph by a strictly monotone decreasing function; then the novel feature extraction criterion based on the intrinsic and penalty graph is applied. For the non-linearly separable problems, we study the kernel extensions of GbFA with respect to positive definite kernels and indefinite kernels, respectively. In addition, experiments are provided for analyzing and illustrating our results.