User menu

Graph Laplacian for Semi-supervised Feature Selection in Regression Problems

Bibliographic reference Doquire, Gauthier ; Verleysen, Michel. Graph Laplacian for Semi-supervised Feature Selection in Regression Problems.11th international work-conference on artificial neural networks (IWANN 2011) (Torremolinos (Spain), du 08/06/2011 au 10/06/2011). In: Joan Cabestany, Advances in Computational Intelligence, Springer2011, p. 248-255
Permanent URL http://hdl.handle.net/2078.1/82292
  1. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J. Mach. Lear. Res. 3, 1157–1182 (2003)
  2. Battiti R., Using mutual information for selecting features in supervised neural net learning, 10.1109/72.298224
  3. Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised Feature Selection Using Feature Similarity. IEEE T. Pattern. Anal. 24 (2002)
  4. He, X., Cai, D., Niyogi, P.: Laplacian Score for Feature Selection. In: Advances in Neural Information Processing Systems (NIPS), vol. 17 (2005)
  5. Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2007)
  6. Zhu, X., Goldberg, A.B.: Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers, San Francisco (2009)
  7. Zhao Zheng, Liu Huan, Semi-supervised Feature Selection via Spectral Analysis, Proceedings of the 2007 SIAM International Conference on Data Mining (2007) ISBN:9780898716306 p.641-646, 10.1137/1.9781611972771.75
  8. Quinzán Ianisse, Sotoca José M., Pla Filiberto, Clustering-Based Feature Selection in Semi-supervised Problems, 10.1109/isda.2009.211
  9. Chung, F.R.K.: Spectral Graph Theory. CBMS Regional Conference Series in Mathematics 92. American Mathematical Society, Providence (1997)
  10. Rossi Fabrice, Delannay Nicolas, Conan-Guez Brieuc, Verleysen Michel, Representation of functional data in neural networks, 10.1016/j.neucom.2004.11.012