Graph-based semi-supervised Support Vector Data Description for novelty detection

Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper, we propose to utilize the information implicated in a spectral graph to leverage SVDD in the context of semi-supervised learning. The theory and experiment evidence that the proposed method is able to efficiently employ the information carried in the spectral graph to not only enhance the generalization ability of SVDD but also enforce the cluster assumption which is crucial for a semi-supervised learning method.

[1]  S. Sathiya Keerthi,et al.  Deterministic annealing for semi-supervised kernel machines , 2006, ICML.

[2]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[3]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[4]  Alexander Zien,et al.  Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.

[5]  Zoubin Ghahramani,et al.  Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning , 2004, NIPS.

[6]  Alexander J. Smola,et al.  Kernels and Regularization on Graphs , 2003, COLT.

[7]  Jason Weston,et al.  Large Scale Transductive SVMs , 2006, J. Mach. Learn. Res..

[8]  S. Sathiya Keerthi,et al.  Optimization Techniques for Semi-Supervised Support Vector Machines , 2008, J. Mach. Learn. Res..

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

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

[12]  Arik Azran,et al.  The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks , 2007, ICML '07.

[13]  Thomas L. Griffiths,et al.  Semi-Supervised Learning with Trees , 2003, NIPS.

[14]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  John D. Lafferty,et al.  Semi-supervised learning using randomized mincuts , 2004, ICML.

[17]  Mikhail Belkin,et al.  Laplacian Support Vector Machines Trained in the Primal , 2009, J. Mach. Learn. Res..

[18]  Trung Le,et al.  Kernel-based semi-supervised learning for novelty detection , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[19]  S. Marsland.  Novelty Detection in Learning Systems , 2008 .

[20]  Avrim Blum,et al.  Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.

[21]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[22]  Colin Campbell,et al.  A Linear Programming Approach to Novelty Detection , 2000, NIPS.

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