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)
Document clustering by concept factorization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Constrained Laplacian Eigenmap for dimensionality reduction
Neurocomputing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Discriminative codeword selection for image representation
Proceedings of the international conference on Multimedia
Locally Consistent Concept Factorization for Document Clustering
IEEE Transactions on Knowledge and Data Engineering
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Graph Regularized Sparse Coding for Image Representation
IEEE Transactions on Image Processing
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Non-negative matrix factorization (NMF) has become a popular technique for finding low-dimensional representations of data. While the standard NMF can only be performed in the original feature space, one variant of NMF, named concept factorization, can be naturally kernelized and inherits all the strengths of NMF. To make use of label information, we propose a semi-supervised concept factorization technique called discriminative concept factorization (DCF) for data representation in this paper. DCF adopts a unified objective to combine the task of data reconstruction with the task of classification. These two tasks have mutual impacts on each other, which results in a concept factorization adapted to the classification task and a classifier built on the low-dimensional representations. Furthermore, we develop an iterative algorithm to solve the optimization problem through alternative convex programming. Experimental results on three real-word classification tasks demonstrate the effectiveness of DCF.