Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm

Authors:
Steve A. Billings;Kian L. Lee
Affiliations:
Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, P.O. Box 600, Sheffield S1 3JD, UK;Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, P.O. Box 600, Sheffield S1 3JD, UK
Venue:
Neural Networks
Year:
2002

Citing 9
Cited 30

The nature of statistical learning theory

The nature of statistical learning theory
Adaptive model selection and estimation for nonlinear systems using a sliding data window

Signal Processing
On-line supervised adaptive training using radial basis function networks

Neural Networks
A Recursive Orthogonal Least Squares Algorithm for Training RBF Networks

Neural Processing Letters
Soft Margins for AdaBoost

Machine Learning
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
Selecting radial basis function network centers with recursive orthogonal least squares training

IEEE Transactions on Neural Networks

Efficient Nonlinear Dimension Reduction for Clustered Data Using Kernel Functions

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Nonlinear Discriminant Analysis by Regularized Minimum Squared Errors

IEEE Transactions on Knowledge and Data Engineering
A fast kernel-based nonlinear discriminant analysis for multi-class problems

Pattern Recognition
An efficient algorithm for generalized discriminant analysis using incomplete Cholesky decomposition

Pattern Recognition Letters
Kernel least-squares models using updates of the pseudoinverse

Neural Computation
A comparison of generalized linear discriminant analysis algorithms

Pattern Recognition
Two-layer automatic sound classification system for conversation enhancement in hearing aids

Integrated Computer-Aided Engineering
A unified framework for semi-supervised dimensionality reduction

Pattern Recognition
Parsimonious Kernel Fisher Discrimination

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Sparse multinomial kernel discriminant analysis (sMKDA)

Pattern Recognition
Updates for nonlinear discriminants

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems

Expert Systems with Applications: An International Journal
Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Pattern Recognition
Kernel-based nonlinear discriminant analysis using minimum squared errors criterion for multiclass and undersampled problems

Signal Processing
Influence of acoustic feedback on the learning strategies of neural network-based sound classifiers in digital hearing aids

EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

The Journal of Machine Learning Research
Letters: Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space

Neurocomputing
An improvement to matrix-based LDA

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
A novel hybrid classification model of artificial neural networks and multiple linear regression models

Expert Systems with Applications: An International Journal
On optimizing kernel-based fisher discriminant analysis using prototype reduction schemes

SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Analysis of Correlation Based Dimension Reduction Methods

International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
Regularized orthogonal linear discriminant analysis

Pattern Recognition
A novel procedure to identify the minimized overlap boundary of two groups by DEA model

ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
An approach for the two-group discriminant analysis: An application of DEA

Mathematical and Computer Modelling: An International Journal
Fast kernel Fisher discriminant analysis via approximating the kernel principal component analysis

Neurocomputing
From the idea of "sparse representation" to a representation-based transformation method for feature extraction

Neurocomputing
Loose particle classification using a new wavelet fisher discriminant method

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Integrated Fisher linear discriminants: An empirical study

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

The nonlinear discriminant function obtained using a minimum squared error cost function can be shown to be directly related to the nonlinear Fisher discriminant (NFD). With the squared error cost function, the orthogonal least squares (OLS) algorithm can be used to find a parsimonious description of the nonlinear discriminant function. Two simple classification techniques will be introduced and tested on a number of real and artificial data sets. The results show that the new classification technique can often perform favourably compared with other state of the art classification techniques.