Pruning Neighborhood Graph for Geodesic Distance Based Semi-Supervised Classification

Recently semi-supervised learning has been gain a surge of interests, but there is a few of research on semi- supervised learning using geodesic distance. The simplest semi-supervised classification algorithm is geodesic nearest neighbors (GNN). However the naive implementation of GNN algorithm is sensitive to the neighborhood scale parameter and suffers from the dilemma of neighborhood scale parameter selection. In this paper, instead of searching for the best neighborhood parameter, we propose a pruned-GNN, which utilize the non-negative reconstructing coefficients to prune the neighborhood graph in order to facilitate the selection of neighborhood scale parameter. Experimental results on several benchmark databases have shown that the proposed pruned-GNN can produce promising accuracies.

[1]  Xinpeng Zhang,et al.  Watermarking Scheme Capable of Resisting Sensitivity Attack , 2007, IEEE Signal Processing Letters.

[2]  Chi-Sung Laih,et al.  On key distribution management for conditional access system on pay-TV system , 1999, IEEE Trans. Consumer Electron..

[3]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[4]  Jean-Paul M. G. Linnartz,et al.  Watermark estimation through detector analysis , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[5]  Claudio Soriente,et al.  A blocker-proof conditional access system , 2004, IEEE Transactions on Consumer Electronics.

[6]  Jean-Paul M. G. Linnartz,et al.  Some general methods for tampering with watermarks , 1998, IEEE J. Sel. Areas Commun..

[7]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[8]  Mohamed F. Mansour,et al.  LMS-based attack on watermark public detectors , 2002, Proceedings. International Conference on Image Processing.

[9]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[10]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[11]  Bernhard Schölkopf,et al.  Learning from Labeled and Unlabeled Data Using Random Walks , 2004, DAGM-Symposium.

[12]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[13]  Jean-Paul M. G. Linnartz,et al.  Analysis of the Sensitivity Attack against Electronic Watermarks in Images , 1998, Information Hiding.

[14]  Fernando Pérez-González,et al.  The Return of the Sensitivity Attack , 2005, IWDW.

[15]  Mikhail Belkin,et al.  Semi-Supervised Learning on Riemannian Manifolds , 2004, Machine Learning.

[16]  Ilaria Venturini Oracle Attacks and Covert Channels , 2005, IWDW.

[17]  H. Sebastian Seung,et al.  The Manifold Ways of Perception , 2000, Science.

[18]  Tommi S. Jaakkola,et al.  Partially labeled classification with Markov random walks , 2001, NIPS.

[19]  Jean-Jacques Quisquater,et al.  Cryptology for digital TV broadcasting , 1995, Proc. IEEE.

[20]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.