Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Semantic manifold learning for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Document clustering with prior knowledge
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Towards a theoretical foundation for laplacian-based manifold methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Human Gait Recognition With Matrix Representation
IEEE Transactions on Circuits and Systems for Video Technology
Discriminative concept factorization for data representation
Neurocomputing
Regularized soft K-means for discriminant analysis
Neurocomputing
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
Constrained spectral embedding for K-way data clustering
Pattern Recognition Letters
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Dimensionality reduction is a commonly used tool in machine learning, especially when dealing with high dimensional data. We consider semi-supervised graph based dimensionality reduction in this paper, and a novel dimensionality reduction algorithm called constrained Laplacian Eigenmap (CLE) is proposed. Suppose the data set contains r classes, and for each class we have some labeled points. CLE maps each data point into r different lines, and each map i tries to separate points belonging to class i from others by using label information. CLE constrains the solution space of Laplacian Eigenmap only to contain embedding results that are consistent with the labels. Then, each point is represented as a r-dimensional vector. Labeled points belonging to the same class are merged together, labeled points belonging to different classes are separated, and similar points are close to one another. We perform semi-supervised document clustering using CLE on two standard corpora. Experimental results show that CLE is very effective.