Latest News and Events

The SAMSI-FODAVA Workshop on Interactive Visualization and Analysis of Massive Data will be held on December 10-12, 2012.
Posted: October 02, 2012
The FODAVA Annual Meeting will immediately follow (Dec 12-13) the SAMSI/FODAVA joint workshop at the same location.
Posted: September 05, 2012
Many of the modern data sets such as text and image data can be represented in high-dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from num
Posted: June 30, 2012

Sparse Subspace Clustering

Rene Vidal

We propose a method based on sparse representation to cluster data
drawn from multiple low-dimensional linear or affine subspaces
embedded in a high-dimensional space. Our method is based on the fact
that each point in a union of subspaces has a sparse representation
with respect to a dictionary formed by all other data points. In
general, finding such a spare representation is NP hard. Our key
contribution is to show that, under mild assumptions, the sparse
representation can be obtained 'exactly' by using $ell_1$
optimization. The segmentation of the data is obtained by applying
spectral clustering to a similarity matrix built from this sparse
representation. Our method can be extended to handle noise, outliers
as well as missing data by exploiting sparsity. Experiments on the
Hopkins155 motion segmentation database and other motion sequences
with outliers and missing data show that our approach significantly
outperforms state-of-the-art methods.