Translated Poisson Mixture Model for Stratification Learning
International Journal of Computer Vision
Spectral Curvature Clustering (SCC)
International Journal of Computer Vision
Geometric Manifold Energy and Manifold Clustering
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Minimum description length and clustering with exemplars
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Robust Algebraic Segmentation of Mixed Rigid-Body and Planar Motions from Two Views
International Journal of Computer Vision
Maximum normalized spacing for efficient visual clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Geometric median-shift over Riemannian manifolds
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Spectral clustering on manifolds with statistical and geometrical similarity
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
A general framework for subspace detection in unordered multidimensional data
Pattern Recognition
Local and structural consistency for multi-manifold clustering
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification
Neural Processing Letters
Neurocomputing
Semi-supervised learning with nuclear norm regularization
Pattern Recognition
A multi-manifold semi-supervised Gaussian mixture model for pattern classification
Pattern Recognition Letters
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Manifold learning has become a vital tool in data driven methods for interpretation of video, motion capture, and handwritten character data when they lie on a low dimensional, non-linear manifold. This work extends manifold learning to classify and parameterize unlabeled data which lie on multiple, intersecting manifolds. This approach significantly increases the domain to which manifold learning methods can be applied, allowing parameterization of example manifolds such as figure eights and intersecting paths which are quite common in natural data sets. This approach introduces several technical contributions which may be of broader interest, including node-weighted multi-dimensional scaling and a fast algorithm for weighted low-rank approximation for rank-one weight matrices. We show examples for intersecting manifolds of mixed topology and dimension and demonstrations on human motion capture data.