Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval
Computer Vision and Image Understanding
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Supervised dimensionality reduction via sequential semidefinite programming
Pattern Recognition
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Discriminatively regularized least-squares classification
Pattern Recognition
A Criterion for Learning the Data-Dependent Kernel for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Locally Linear Discriminant Embedding for Tumor Classification
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
EURASIP Journal on Advances in Signal Processing
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Classification by discriminative regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
IEEE Transactions on Neural Networks
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Sparsity preserving discriminant analysis for single training image face recognition
Pattern Recognition Letters
Outlier-resisting graph embedding
Neurocomputing
Locality preserving discriminant projections
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Maximum margin criterion with tensor representation
Neurocomputing
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
On the relevance of linear discriminative features
Information Sciences: an International Journal
Linear discriminant projection embedding based on patches alignment
Image and Vision Computing
Artificial Intelligence Review
HOG-based approach for leaf classification
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Orthogonal discriminant local tangent space alignment
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Orthogonal local spline discriminant projection with application to face recognition
Pattern Recognition Letters
Feature Extraction Using Laplacian Maximum Margin Criterion
Neural Processing Letters
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis
SIAM Journal on Matrix Analysis and Applications
Locally linear embedding: a survey
Artificial Intelligence Review
Correntropy based feature selection using binary projection
Pattern Recognition
Graph descriptors from B-matrix representation
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Expert Systems with Applications: An International Journal
Robust linearly optimized discriminant analysis
Neurocomputing
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Trace quotient problems revisited
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Feature extraction via balanced average neighborhood margin maximization
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Feature extraction using fuzzy maximum margin criterion
Neurocomputing
Sparse maximum margin discriminant analysis for gene selection
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Optimal regularization parameter estimation for regularized discriminant analysis
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Optimized projection for sparse representation based classification
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding
Neural Processing Letters
Incremental learning of complete linear discriminant analysis for face recognition
Knowledge-Based Systems
Face recognition using discriminant sparsity neighborhood preserving embedding
Knowledge-Based Systems
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
A novel maximum margin neighborhood preserving embedding for face recognition
Future Generation Computer Systems
Enhanced semi-supervised local Fisher discriminant analysis for face recognition
Future Generation Computer Systems
Invariants of distance k-graphs for graph embedding
Pattern Recognition Letters
Towards collaborative feature extraction for face recognition
Natural Computing: an international journal
Sparse discriminating neighborhood preserving embedding
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Enhanced eigenspace separation transform for classification
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Kernel based enhanced maximum margin criterion for feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
A competitive model for semi-supervised discriminant analysis
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Nearest feature line discriminant analysis in DFRCT domain for image feature extraction
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis
Information Sciences: an International Journal
Discriminant analysis based on nearest feature line
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Robust gait recognition via discriminative set matching
Journal of Visual Communication and Image Representation
Computers in Biology and Medicine
Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction
ACM Transactions on Knowledge Discovery from Data (TKDD)
A Rayleigh-Ritz style method for large-scale discriminant analysis
Pattern Recognition
Expert Systems with Applications: An International Journal
Orthogonal locally discriminant spline embedding for plant leaf recognition
Computer Vision and Image Understanding
Weighted discriminative sparsity preserving embedding for face recognition
Knowledge-Based Systems
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem . In this paper, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.