Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace maximum margin clustering
Proceedings of the 18th ACM conference on Information and knowledge management
Locality preserving nonnegative matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Local learning regularized nonnegative matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Constrained Laplacian Eigenmap for dimensionality reduction
Neurocomputing
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Manifold-respecting discriminant nonnegative matrix factorization
Pattern Recognition Letters
A compression-based dissimilarity measure for multi-task clustering
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Cholesky decomposition rectification for non-negative matrix factorization
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Topic graph based non-negative matrix factorization for transfer learning
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Multi-task clustering via domain adaptation
Pattern Recognition
Robust nonnegative matrix factorization using L21-norm
Proceedings of the 20th ACM international conference on Information and knowledge management
Temporal link prediction by integrating content and structure information
Proceedings of the 20th ACM international conference on Information and knowledge management
Extracting non-negative basis images using pixel dispersion penalty
Pattern Recognition
Review article: Max-margin Non-negative Matrix Factorization
Image and Vision Computing
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
On trivial solution and scale transfer problems in graph regularized NMF
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Fast nonnegative matrix tri-factorization for large-scale data co-clustering
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Neighborhood preserving hashing for fast similarity search
Proceedings of the 20th ACM international conference on Multimedia
Relational co-clustering via manifold ensemble learning
Proceedings of the 21st ACM international conference on Information and knowledge management
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
Spatially correlated nonnegative matrix factorization for image analysis
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Social trust prediction using heterogeneous networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization
Engineering Applications of Artificial Intelligence
Rectifying the representation learned by Non-negative Matrix Factorization
International Journal of Knowledge-based and Intelligent Engineering Systems
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Recently Non-negative Matrix Factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. In this paper, we develop a graph based approach for parts-based data representation in order to overcome this limitation. We construct an affinity graph to encode the geometrical information and seek a matrix factorization which respects the graph structure. We demonstrate the success of this novel algorithm by applying it on real world problems.