Pairwise support vector machines and their application to large scale problems

Pairwise classification is the task to predict whether the examples a,b of a pair (a,b) belong to the same class or to different classes. In particular, interclass generalization problems can be treated in this way. In pairwise classification, the order of the two input examples should not affect the classification result. To achieve this, particular kernels as well as the use of symmetric training sets in the framework of support vector machines were suggested. The paper discusses both approaches in a general way and establishes a strong connection between them. In addition, an efficient implementation is discussed which allows the training of several millions of pairs. The value of these contributions is confirmed by excellent results on the labeled faces in the wild benchmark.

[1]  Tomer Hertz,et al.  Boosting margin based distance functions for clustering , 2004, ICML.

[2]  Daphna Weinshall,et al.  Learning distance function by coding similarity , 2007, ICML '07.

[3]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[4]  Andreas Fischer,et al.  Pairwise Kernels, Support Vector Machines, and the Application to Large Scale Problems , 2011 .

[5]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

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

[7]  S. Sathiya Keerthi,et al.  Which Is the Best Multiclass SVM Method? An Empirical Study , 2005, Multiple Classifier Systems.

[8]  M. Gamassi,et al.  Accuracy and performance of biometric systems , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[9]  William Stafford Noble,et al.  Kernel methods for predicting protein-protein interactions , 2005, ISMB.

[10]  Francis R. Bach,et al.  A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization , 2008, J. Mach. Learn. Res..

[11]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[12]  P. Jonathon Phillips,et al.  Support Vector Machines Applied to Face Recognition , 1998, NIPS.

[13]  Robert M. Nishikawa,et al.  Learning of Perceptual Similarity From Expert Readers for Mammogram Retrieval , 2009, IEEE Journal of Selected Topics in Signal Processing.

[14]  Tal Hassner,et al.  Similarity Scores Based on Background Samples , 2009, ACCV.

[15]  Arnaud Doucet,et al.  A Framework for Kernel-Based Multi-Category Classification , 2007, J. Artif. Intell. Res..

[16]  Tomer Hertz,et al.  Learning distance functions for image retrieval , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[17]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[18]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[19]  William Stafford Noble,et al.  A new pairwise kernel for biological network inference with support vector machines , 2007, BMC Bioinformatics.

[20]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[21]  Yaniv Taigman,et al.  Descriptor Based Methods in the Wild , 2008 .

[22]  Daphna Weinshall,et al.  Learning distance functions for image retrieval , 2004, CVPR 2004.

[23]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Cordelia Schmid,et al.  Is that you? Metric learning approaches for face identification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[25]  Umar Mohammed,et al.  Probabilistic Models for Inference about Identity , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.