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IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal Neighborhood Preserving Projections
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Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Efficient and robust feature extraction by maximum margin criterion
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Enhanced supervised locally linear embedding
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Two-dimensional discriminant locality preserving projections for face recognition
Pattern Recognition Letters
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Locality preserving discriminant projections
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Feature fusion using locally linear embedding for classification
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Linear discriminant projection embedding based on patches alignment
Image and Vision Computing
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AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Guided Locally Linear Embedding
Pattern Recognition Letters
Locally linear embedding: a survey
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Weighted principal component extraction with genetic algorithms
Applied Soft Computing
A novel maximum margin neighborhood preserving embedding for face recognition
Future Generation Computer Systems
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
Chaotic random projection for cancelable biometric key generation
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
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Multimedia Tools and Applications
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Computers in Biology and Medicine
Tumor gene expressive data classification based on locally linear representation fisher criterion
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.