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Efficient dense subspace clustering

Ji, Pan; Salzmann, Mathieu; Li, Hongdong


In this paper, we tackle the problem of clustering data points drawn from a union of linear (or affine) subspaces. To this end, we introduce an efficient subspace clustering algorithm that estimates dense connections between the points lying in the same subspace. In particular, instead of following the standard compressive sensing approach, we formulate subspace clustering as a Frobenius norm minimization problem, which inherently yields denser con- nections between the data points. While in...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Conference paper
Source: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
DOI: 10.1109/WACV.2014.6836065


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