Abstract
In many applications, we need to cluster large-scale data objects. However, some recently proposed clustering algorithms such as spectral clustering can hardly handle large-scale applications due to the complexity issue, although their effectiveness has been demonstrated in previous work. In this paper, we propose a fast solver for spectral clustering. In contrast to traditional spectral clustering algorithms that first solve an eigenvalue decomposition problem, and then employ a clustering heuristic to obtain labels for the data points, our new approach sequentially decides the labels of relatively well-separated data points. Because the scale of the problem shrinks quickly during this process, it can be much faster than the traditional methods. Experiments on both synthetic data and a large collection of product records show that our algorithm can achieve significant improvement in speed as compared to traditional spectral clustering algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bunk, B.: Conjugate Gradient Algorithm to Compute the Low-lying Eigenvalues of the Dirac Operator in Lattice QCD. Computer Physics Communication (1994)
Ding, C.: A Tutorial on Spectral Clustering. In: ICML 21 (2004)
Feng, Y.T., Owen, D.R.J.: Conjugate Gradient Methods for Solving the Smallest Eigenpair of Large Symmetric Eigenvalue Problems. International Journal for Numerical Methods in Engineering (1996)
Golub, G.H., Loan, C.F.V.: Matrix Computations. John Hopkins University Press, Baltimore (1996)
Knyazev, A.V.: Toward the Optimal Preconditioned Eigensolver: Locally Optimal Block Preconditioned Conjugate Gradient Method. SIAM Journal of Scientific Computing (2001)
Knyazev, A.V.: Preconditioned eigensolvers: practical algorithms. Technical Report: UCD-CCM 143, University of Colorado at Denver (1999)
Nocedal, J., Wright, S.J.: Numerical Optimization. Series in Operations Research. Springer, Heidelberg (2000)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On Spectral Clustering: Analysis and an Algorithm. In: NIPS 14 (2001)
Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Sorensen, D.C.: Implicitly Restarted Arnoldi/Lanczos Methods for Large Scale Eigen-value Calculations. In: Parallel Numerical Algorithms (1995)
Stanimire, T., Langou, J., Canning, A., et al.: Conjugate-Gradient Eigenvalue Solvers in Computing Electronic Properties of Nanostructure Architectures. Technical Report, UT-CS-05-559, The University of Tennessee Knoxville (2005)
Yang, X.P., Sarkar, T.K., Arvas, E.: A Survey of Conjugate Gradient Algorithms for Solution of Extreme Eigen-problems of a Symmetric Matrix. IEEE Transactions on Acoustics, Speech, and Signal Procesing 37, 1550–1556 (1989)
Yu, S.X., Shi, J.B.: Multi-class Spectral Clustering. In: ICCV 9 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Liu, TY., Yang, HY., Zheng, X., Qin, T., Ma, WY. (2007). Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-71496-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71494-1
Online ISBN: 978-3-540-71496-5
eBook Packages: Computer ScienceComputer Science (R0)