Abstract
Clustering based on sparse representation is an important technique in machine learning and data mining fields. However, it is time-consuming because it constructs l 1-graph by solving l 1-minimization with all other samples as dictionary for each sample. This paper is focused on improving the efficiency of clustering based on sparse representation. Specifically, the Spectral Clustering Algorithm Based on Local Sparse Representation (SCAL) is proposed. For a given sample the algorithm solves l 1-minimization with the local \(\emph{k}\) nearest neighborhood as dictionary, constructs the similarity matrix by calculating sparsity induced similarity (SIS) of the sparse coefficients solution, and then uses spectral clustering with the similarity matrix to cluster the samples. Experiments using face recognition data sets ORL and Extended Yale B demonstrate that the proposed SCAL can get better clustering performance and less time consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Warren Liao, T.: Clustering of time series data-a survey. Pattern Recognition 38, 1857–1874 (2005)
Gao, Y., Choudhary, A., Hua, G.: A nonnegative sparsity induced similarity measure with application to cluster analysis of spam images. In: 2010 IEEE International Conference on Acoustics Speech and Signal Pro-cessing (ICASSP), pp. 5594–5597. IEEE (2010)
Yu, Z., Li, L., You, J., Wong, H., Han, G.: SC3: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 9, 1751–1765 (2012)
Wang, H., Chen, J., Huang, S., Guo, K.: A Heuristic Initialization-Independent Spectral Clustering. In: 2010 Fifth International Conference on Internet Computing for Science and Engineering (ICICSE), pp. 81–84. IEEE (2010)
Li, C., Guo, J., Zhang, H.: Local sparse representation based classification. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 649–652. IEEE (2010)
Yan, S., Wang, H.: Semi-supervised Learning by Sparse Representation. In: 2009 SIAM Conference on Data Mining, pp. 792–801 (2009)
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 2790–2797. IEEE (2009)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009)
Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. P IEEE 98, 1031–1044 (2010)
Jiao, J., Mo, X., Shen, C.: Image clustering via sparse representation. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, Y.-P.P. (eds.) MMM 2010. LNCS, vol. 5916, pp. 761–766. Springer, Heidelberg (2010)
Cheng, H., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: 2009 IEEE 12th International Conference on Image clustering via sparse representation, pp. 317–324. IEEE (2009)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 2, pp. 849–856 (2002)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Advances in Neural Information Processing Systems, pp. 2223–2231 (2009)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering 20, 55–67 (2008)
Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: A method for large-scale l1-regularized least squares. IEEE Journal on Selected Topics in Signal Processing 4, 606–617 (2007)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Disc. 2, 283–304 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, S., Quan, M., Feng, X. (2013). Spectral Clustering Algorithm Based on Local Sparse Representation. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-41278-3_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41277-6
Online ISBN: 978-3-642-41278-3
eBook Packages: Computer ScienceComputer Science (R0)