Spectral Curvature Clustering (SCC)
Spectral Curvature Clustering (SCC) is a multi-way spectral clustering algorithm for
solving the problem of hybrid linear modeling, that
is, to model and segment data using an arrangement of affine subspaces.
For the justification of the
algorithm and its underlying theory, please refer to the FoCM paper
below; for the practical techniques and numerical results, please see
the IJCV paper below.
- Motion Segmentation for Hopkins 155 Database Via SCC [PDF], G. Chen and G.
Lerman, The 4th ICCV Workshop on Dynamical Vision, September 2009,
- Foundations of a
Multi-way Spectral Clustering Framework for Hybrid Linear Modeling
[PDF], G. Chen
and G. Lerman, Found. Comput. Math. (2009) 9: 517–558, DOI
- Spectral Curvature
Clustering (SCC) [PDF],
G. Chen and G.
Lerman, Int. J. Comput. Vis. (2009) 81:317-330, DOI
(implemented by the authors, with some help from Stefan Atev to improve speed)
- Other algorithms
compared with in the IJCV paper:
SCC (implemented by Amit Hooda; have
NOT been tested by the authors)
Extensions to Multi-Manifold Data Modeling
- The research described here was supported by NSF grant #0612608.
Last updated on 11/11/2011. Maintained by Guangliang Chen.