NDLI: Time and space efficient spectral clustering via column sampling

Content Provider	IEEE Xplore Digital Library
Author	Mu Li Xiao-Chen Lian Kwok, J.T. Bao-Liang Lu
Copyright Year	2011
Description	Author affiliation: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China (Mu Li; Xiao-Chen Lian; Bao-Liang Lu) \|\| Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong (Kwok, J.T.)
Abstract	Spectral clustering is an elegant and powerful approach for clustering. However, the underlying eigen-decomposition takes cubic time and quadratic space w.r.t. the data set size. These can be reduced by the Nyström method which samples only a subset of columns from the matrix. However, the manipulation and storage of these sampled columns can still be expensive when the data set is large. In this paper, we propose a time- and space-efficient spectral clustering algorithm which can scale to very large data sets. A general procedure to orthogonalize the approximated eigenvectors is also proposed. Extensive spectral clustering experiments on a number of data sets, ranging in size from a few thousands to several millions, demonstrate the accuracy and scalability of the proposed approach. We further apply it to the task of image segmentation. For images with more than 10 millions pixels, this algorithm can obtain the eigenvectors in 1 minute on a single machine.
Starting Page	2297
Ending Page	2304
File Size	5299189
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Clustering algorithms Approximation algorithms Complexity theory Laplace equations Eigenvalues and eigenfunctions Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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