Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Parts-Based Representations of Data
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using underapproximations for sparse nonnegative matrix factorization
Pattern Recognition
Generalized re-weighting local sampling mean discriminant analysis
Pattern Recognition
Robust Positive semidefinite L-Isomap Ensemble
Pattern Recognition Letters
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast affinity propagation clustering: A multilevel approach
Pattern Recognition
Radar HRRP target recognition based on higher order spectra
IEEE Transactions on Signal Processing
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks
Fast semi-supervised clustering with enhanced spectral embedding
Pattern Recognition
A fast tri-factorization method for low-rank matrix recovery and completion
Pattern Recognition
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification
Neural Processing Letters
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Low-rank matrix factorization is one of the most useful tools in scientific computing, data mining and computer vision. Among of its techniques, non-negative matrix factorization (NMF) has received considerable attention due to producing a parts-based representation of the data. Recent research has shown that not only the observed data are found to lie on a nonlinear low dimensional manifold, namely data manifold, but also the features lie on a manifold, namely feature manifold. In this paper, we propose a novel algorithm, called graph dual regularization non-negative matrix factorization (DNMF), which simultaneously considers the geometric structures of both the data manifold and the feature manifold. We also present a graph dual regularization non-negative matrix tri-factorization algorithm (DNMTF) as an extension of DNMF. Moreover, we develop two iterative updating optimization schemes for DNMF and DNMTF, respectively, and provide the convergence proofs of our two optimization schemes. Experimental results on UCI benchmark data sets, several image data sets and a radar HRRP data set demonstrate the effectiveness of both DNMF and DNMTF.