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Introduction to statistical pattern recognition (2nd ed.)
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Optimal dimensionality of metric space for classification
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Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Extracting the optimal dimensionality for local tensor discriminant analysis
Pattern Recognition
Emerging Trends in Visual Computing
Learning by local kernel polarization
Neurocomputing
Radon representation-based feature descriptor for texture classification
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Hierarchical Multi-view Fisher Discriminant Analysis
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Web image annotation based on automatically obtained noisy training set
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Automatic detection and recognition of Korean text in outdoor signboard images
Pattern Recognition Letters
Artificial Intelligence Review
Robust head pose estimation using supervised manifold learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Manifold-respecting discriminant nonnegative matrix factorization
Pattern Recognition Letters
Linear discriminant dimensionality reduction
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Analysis of Correlation Based Dimension Reduction Methods
International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
Pattern Recognition Letters
A fusion approach to unconstrained iris recognition
Pattern Recognition Letters
l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Maxi-Min discriminant analysis via online learning
Neural Networks
Self-adaptive local Fisher discriminant analysis for semi-supervised image recognition
International Journal of Biometrics
K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis
Information Sciences: an International Journal
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
Towards the Optimal Discriminant Subspace
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by the kernel trick.