Skip navigation
Skip navigation

Efficient dense subspace clustering

Ji, Pan; Salzmann, Mathieu; Li, Hongdong

Description

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
URI: http://hdl.handle.net/1885/74616
Source: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
DOI: 10.1109/WACV.2014.6836065

Download

File Description SizeFormat Image
01_Ji_Efficient_dense_subspace_2014.pdf886.66 kBAdobe PDF    Request a copy


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  20 July 2017/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator