Neighborhood discriminant projection for face recognition
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal dimensionality of metric space for classification
Proceedings of the 24th international conference on Machine learning
Metric learning by discriminant neighborhood embedding
Pattern Recognition
Noisy manifold learning using neighborhood smoothing embedding
Pattern Recognition Letters
Extracting the optimal dimensionality for local tensor discriminant analysis
Pattern Recognition
Structure feature selection for graph classification
Proceedings of the 17th ACM conference on Information and knowledge management
Emerging Trends in Visual Computing
Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
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Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Locality sensitive discriminant analysis
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Neighbourhood preserving discriminant embedding in face recognition
Journal of Visual Communication and Image Representation
Locating nose-tips and estimating head poses in images by tensorposes
IEEE Transactions on Circuits and Systems for Video Technology
Proximal support vector machine using local information
Neurocomputing
Laplacian Discriminant Projection Based on Affinity Propagation
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Constrained Laplacian Eigenmap for dimensionality reduction
Neurocomputing
A unified framework of subspace and distance metric learning for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Analyzing facial expression by fusing manifolds
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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LPP solution schemes for use with face recognition
Pattern Recognition
Dimensionality reduction by using sparse reconstruction embedding
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Uncorrelated trace ratio linear discriminant analysis for undersampled problems
Pattern Recognition Letters
Fast Algorithms for the Generalized Foley-Sammon Discriminant Analysis
SIAM Journal on Matrix Analysis and Applications
Manifold topological multi-resolution analysis method
Pattern Recognition
Face recognition based on gabor enhanced marginal fisher model and error correction SVM
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Face recognition based on gabor-enhanced manifold learning and SVM
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
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
Exemplar based laplacian discriminant projection
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Generalized local discriminant embedding for face recognition
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Data-based modeling and monitoring for multimode processes using local tangent space alignment
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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
Image classification with manifold learning for out-of-sample data
Signal Processing
Robust gait recognition via discriminative set matching
Journal of Visual Communication and Image Representation
Parameterless Local Discriminant Embedding
Neural Processing Letters
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In the last decades, a large family of algorithms 驴. supervised or unsupervised; stemming from statistic or geometry theory..have been proposed to provide different solutions to the problem of dimensionality reduction. In this paper, beyond the different motivations of these algorithms, we propose a general framework, graph embedding along with its linearization and kernelization, which in theory reveals the underlying objective shared by most previous algorithms. It presents a unified perspective to understand these algorithms; that is, each algorithm can be considered as the direct graph embedding or its linear/kernel extension of some specific graph characterizing certain statistic or geometry property of a data set. Furthermore, this framework is a general platform to develop new algorithm for dimensionality reduction. To this end, we propose a new supervised algorithm, Marginal Fisher Analysis (MFA), for dimensionality reduction by designing two graphs that characterize the intra-class compactness and inter-class separability, respectively. MFA measures the intra- class compactness with the distance between each data point and its neighboring points of the same class, and measures the inter-class separability with the class margins; thus it overcomes the limitations of traditional Linear Discriminant Analysis algorithm in terms of data distribution assumptions and available projection directions. The toy problem on artificial data and the real face recognition experiments both show the superiority of our proposed MFA in comparison to LDA.