Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Bilinear analysis for kernel selection and nonlinear feature extraction.

Shu Yang¹, Shuicheng Yan, Chao Zhang

¹Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. sophyshu@gmail.com

IEEE Transactions on Neural Networks

|January 29, 2008

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Vibrational and structural properties of tetramethyltin under pressure.

The Journal of chemical physics·2013

Same author

[Risk factors on the unintentional injuries among rural children aged 0-12 in Shaanxi province].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2013

Same author

Adcyap1r1 genotype, posttraumatic stress disorder, and depression among women exposed to childhood maltreatment.

Depression and anxiety·2013

Same author

Current status and challenge of Human Parasitology teaching in China.

Pathogens and global health·2012

Same author

Molecular characterization of heterogeneous mesenchymal stem cells with single-cell transcriptomes.

Biotechnology advances·2012

Same author

Surgical treatment of ossification of the ligamentum flavum associated with dural ossification in the thoracic spine.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2012

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

See all related articles

This study introduces the Fisher + kernel criterion (FKC) for advanced feature extraction and recognition. This method enhances nonlinear discriminant analysis and kernel selection, improving face recognition accuracy.

Area of Science:

Computer Science
Machine Learning
Pattern Recognition

Background:

Traditional kernel discriminant analysis (KDA) faces challenges with ill-posed problems and singularities.
Effective feature extraction in nonlinear spaces is crucial for robust recognition systems.

Purpose of the Study:

To propose a unified criterion, Fisher + kernel criterion (FKC), for simultaneous nonlinear discriminant analysis and kernel selection.
To develop an efficient algorithm, Fisher + kernel analysis (FKA), for optimizing FKC.

Main Methods:

Developed the Fisher + kernel criterion (FKC) to extract and fuse discriminant features from nonlinear spaces.
Implemented the Fisher + kernel analysis (FKA) algorithm using bilinear analysis for criterion optimization.
Addressed and alleviated ill-posed problems and singularity issues inherent in traditional KDA.

Related Experiment Videos

Main Results:

The FKC effectively extracts discriminant features in nonlinear spaces.
The FKA algorithm efficiently optimizes the FKC, demonstrating robustness against singularity problems.
Validated the proposed method's effectiveness through extensive face recognition experiments on multiple databases.

Conclusions:

The proposed FKC and FKA offer a unified and efficient approach for feature extraction and recognition.
The method shows significant improvements in face recognition tasks, outperforming traditional KDA.
FKC and FKA provide a robust solution for handling nonlinearities and improving discriminant analysis.