Fuzzy entropy semi-supervised support vector data description

Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.

[1]  Alexander Zien,et al.  A continuation method for semi-supervised SVMs , 2006, ICML.

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

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

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Dale Schuurmans,et al.  Maximum Margin Clustering , 2004, NIPS.

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

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

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

[9]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

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

[11]  Tijl De Bie,et al.  Semi-Supervised Learning Using Semi-Definite Programming , 2006, Semi-Supervised Learning.

[12]  Dae-Won Kim,et al.  Density-Induced Support Vector Data Description , 2007, IEEE Transactions on Neural Networks.

[13]  Ayhan Demiriz,et al.  Semi-Supervised Support Vector Machines , 1998, NIPS.

[14]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[15]  O. Mangasarian,et al.  Semi-superyised support vector machines for unlabeled data classification , 2001 .

[16]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..