Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Face recognition using neighborhood preserving projections
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Neighborhood preserving projections (NPP): a novel linear dimension reduction method
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Adaptive kernel principal component analysis
Signal Processing
Shot boundary detection based on supervised locality preserving projections and KNN-SVM classifier
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Multiple kernel local Fisher discriminant analysis for face recognition
Signal Processing
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Dimensionality reduction is a crucial step for pattern recognition tasks and finding a suitable low-dimensional subspace has an important effect on recognition performance. Recently, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of dimensionality reduction method, manifold learning. Among them, neighborhood preserving projections (NPP) is one of the most promising techniques. Based on NPP, we propose a novel nonlinear dimensionality reduction algorithm, called supervised kernel neighborhood preserving projections (SKNPP), which aims at preserving the local manifold structures defined by within-class samples in some high-dimensional feature space. SKNPP can not only gain a perfect nonlinear approximation of data manifold through kernel technique, but also enhance the local within-class relations by taking into account class label information. The proposed SKNPP is compared with NPP, LDA and KLDA on radar target recognition with range profiles. Experimental results indicate the promising recognition performance of the proposed method.