Abstract
Laplacian Score (LS) is a popular feature ranking based feature selection method both supervised and unsupervised. In this paper, we propose an improved LS method called Iterative Laplacian Score (IterativeLS), based on iteratively updating the nearest neighborhood graph for evaluating the importance of a feature by its locality preserving ability. Compared with LS, the key idea of IterativeLS is to gradually improve the nearest neighbor graph by discarding the least relevant features at each iteration. Experimental results on several high dimensional data sets demonstrate the effectiveness of our proposed method.
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
Belkin, M., Niyogi, P.: Laplacian Egienmaps and Spectral Techniques for Embedding and Clustering. In: Advances in Neural Information Processing System, vol. 14 (2001)
Dy, J.G., Brodley, C.E., Kak, A.C., Broderick, L.S., Aisen, A.M.: Unsupervised feature selection applied to content-based retrieval of lung image. IEEE Trans. Pattern Anal. Mach Intell. 25, 373–378 (2003)
Dy, J.G., Brodley, C.E.: Feature selection for unsupervised feature learning. J. March. Learning Res. 5, 845–889 (2004)
He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing System, vol. 17. MIT press, Cambridge (2005)
He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing System, vol. 16. MIT Press, Cambridge (2004)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Xu, W., Liu, X., Gong, Y.: Document Clustering Based on Non-negative Matrix Factorization. In: ACM SIGIR Conference on Information Retrieval (2003)
Sun, Y., Todorovic, S., Goodison, S.: Local-learning-Based Feature Selection for High-Dimensional Data Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligent 32(9) (2010)
Xu, J., Man, H.: Dictionary Learning Based on Laplacian Score in Sparse Coding. In: Perner, P. (ed.) MLDM 2011. LNCS (LNAI), vol. 6871, pp. 253–264. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, L., Miao, L., Zhang, D. (2012). Iterative Laplacian Score for Feature Selection. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
eBook Packages: Computer ScienceComputer Science (R0)