Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Global Optimization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Manifold Clustering By Dimensionality
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Linear manifold clustering in high dimensional spaces by stochastic search
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
An Algorithm for Finding Intrinsic Dimensionality of Data
IEEE Transactions on Computers
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
International Journal of Computer Vision
Minimum effective dimension for mixtures of subspaces: a robust GPCA algorithm and its applications
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Intrinsic dimension induced similarity measure for clustering
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Local and structural consistency for multi-manifold clustering
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Multi-manifold model of the Internet delay space
Journal of Network and Computer Applications
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Manifold clustering, which regards clusters as groups of points around compact manifolds, has been realized as a promising generalization of traditional clustering. A number of linear or nonlinear manifold clustering approaches have been developed recently. Although they have attained better performances than traditional clustering methods in many scenarios, most of these approaches suffer from two weaknesses. First, when the data are drawn from hybrid modeling, i.e., some data manifolds are separated but some are intersected, existing approaches could not work well although hybrid modeling often appears in real data. Second, many approaches require to know the number of clusters and the intrinsic dimensions of the manifolds in advance, while it is hard for the user to provide such information in practice. In this paper, we propose a new manifold clustering approach, mumCluster, to address these issues. Experimental results show that the performance of the proposed mumCluster approach is encouraging.