Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
A dependence maximization view of clustering
Proceedings of the 24th international conference on Machine learning
On the value of pairwise constraints in classification and consistency
Proceedings of the 24th international conference on Machine learning
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of browsing models for content based image retrieval
Multimedia Tools and Applications
Sparse Super Symmetric Tensor Factorization
Neural Information Processing
Data Clustering with Semi-binary Nonnegative Matrix Factorization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Regularization on Graphs with Function-adapted Diffusion Processes
The Journal of Machine Learning Research
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
RK-Means Clustering: K-Means with Reliability
IEICE - Transactions on Information and Systems
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02
Regularized data fusion improves image segmentation
Proceedings of the 29th DAGM conference on Pattern recognition
A noise-insensitive object tracking algorithm
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Pairwise probabilistic clustering using evidence accumulation
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Efficient clustering earth mover's distance
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Multi-way clustering using super-symmetric non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Multistability of α-divergence based NMF algorithms
Computers & Mathematics with Applications
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We derive the clustering problem from first principles showing that the goal of achieving a probabilistic, or "hard", multi class clustering result is equivalent to the algebraic problem of a completely positive factorization under a doubly stochastic constraint. We show that spectral clustering, normalized cuts, kernel K-means and the various normalizations of the associated affinity matrix are particular instances and approximations of this general principle. We propose an efficient algorithm for achieving a completely positive factorization and extend the basic clustering scheme to situations where partial label information is available.