On the exponential value of labeled samples
Pattern Recognition Letters
Machine Learning
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
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
The Journal of Machine Learning Research
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
A Semi-Supervised Framework for Mapping Data to the Intrinsic Manifold
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Supervised dimensionality reduction using mixture models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
Closed-form supervised dimensionality reduction with generalized linear models
Proceedings of the 25th international conference on Machine learning
A new shape descriptor defined on the Radon transform
Computer Vision and Image Understanding
Ubiquitously supervised subspace learning
IEEE Transactions on Image Processing
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Locality preserving and global discriminant projection with prior information
Machine Vision and Applications
Supervised learning on local tangent space
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Activity recognition via classification constrained diffusion maps
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Semi-supervised learning has recently received considerable attention in machine learning. In this paper, we propose a novel diffusion maps based semi-supervised algorithm for dimensionality reduction, visualization and data representation. Unlike previous work which uses only geometric information for similarity metric construction, a distributional similarity metric is introduced to modify the geometric relationship of samples. This metric is defined using the posterior probability over the labels of each sample, which is learned through the Expectation-Maximization (EM) algorithm. The Euclidean distance between points on the intrinsic manifold learned by our proposed method is equal to the label-dependent ''diffusion distance'', which is modified by the distributional similarity related metric, in the original space. Our algorithm preserves the local manifold structure in addition to separating samples in different classes, thus facilitates the classification. Encouraging experimental results on handwritten digits, Yale faces, UCI data sets and the Weizmann data set show that the algorithm can improve the classification accuracy significantly.