Journal ArticleDOI
Kernel self-optimization learning for kernel-based feature extraction and recognition
TLDR
In this paper, a uniform framework for kernel self-optimization with the ability to adjust the data structure is presented, where the data-dependent kernel is extended and applied to kernel learning, and optimization equations with two criteria for measuring data discrimination are used to solve the optimal parameter values.About:
This article is published in Information Sciences.The article was published on 2014-02-01. It has received 18 citations till now. The article focuses on the topics: Kernel principal component analysis & Radial basis function kernel.read more
Citations
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Journal ArticleDOI
Learning a discriminant graph-based embedding with feature selection for image categorization.
TL;DR: A novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS), which aims to classify image sample data in supervised learning and semi-supervised learning settings and compares favorably with many competing embedding methods.
Proceedings ArticleDOI
A new ensemble of features for breast cancer diagnosis
TL;DR: An automatic Computer Aided Diagnosis (CAD) system is completely designed for breast cancer diagnosis and it is verified on a publicly available mammogram dataset constructed during Image Retrieval in Medical Applications (IRMA) project.
Journal ArticleDOI
A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis
TL;DR: The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis and the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two and three-stage studies.
Journal ArticleDOI
KPCA method based on within-class auxiliary training samples and its application to pattern classification
TL;DR: A simple yet effective strategy to improve the performance of PCA is proposed and then this strategy is generalized to KPCA and the proposed methods have more discriminant information.
Journal ArticleDOI
Modified Kernel Marginal Fisher Analysis for Feature Extraction and Its Application to Bearing Fault Diagnosis
Li Jiang,Shunsheng Guo +1 more
TL;DR: In this paper, a modified kernel marginal Fisher analysis (MKMFA) was proposed for feature extraction with dimensionality reduction, which is capable of effectively extracting the sensitive low-dimensional manifold characteristics beneficial to subsequent pattern classification even for few training samples.
References
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Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Generalized Discriminant Analysis Using a Kernel Approach
G. Baudat,F. Anouar +1 more
TL;DR: A new method that is close to the support vector machines insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space to deal with nonlinear discriminant analysis using kernel function operator.
The Mammographic Image Analysis Society digital mammogram database
John Suckling,J. Parker,S. Astley,I. Hutt,C. Boggis,Ian W. Ricketts,E. Stamatakis,N. Cerneaz,SL Kok,P. Taylor,D. Betal,J. Savage +11 more
Journal ArticleDOI
Improving support vector machine classifiers by modifying kernal functions
Shun-ichi Amari,S. Wu +1 more
TL;DR: Simulation results for both artificial and real data show remarkable improvement of generalization errors, supporting the idea of modifying a kernel function to enlarge the spatial resolution around the separating boundary surface by a conformal mapping, such that the separability between classes is increased.
Proceedings ArticleDOI
Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods
TL;DR: Experimental results show that kernel methods provide better representations and achieve lower error rates for face recognition, which are compared with classical algorithms such as Eigenface, Fisherface, ICA, and Support Vector Machine.