Enhanced Sample Selection for SVM on Face Recognition

For SVMs, large training samples will lead to high computing complexity of convex quadratic programming, even difficulty in running. Sample selection as a preprocessor of classification can greatly reduce the computational cost of training and test. In this paper, we present an enhanced sample selection frame based on convex structure for SVM. By learning the approximate errors of chosen set, we realize automatic control of sample scale for SVMs. Experimental results on face recognition show that our sample selection methods can adaptively select fewer high quality samples while maintaining the classification accuracy of SVM.

[1]  David J. Crisp,et al.  A Geometric Interpretation of v-SVM Classifiers , 1999, NIPS.

[2]  Feng Yan,et al.  A fast training algorithm for support vector machine via boundary sample selection , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[3]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[4]  Daphne Koller,et al.  Support Vector Machine Active Learning with Application sto Text Classification , 2000, ICML.

[5]  Hyunsoo Kim,et al.  Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model , 2004, SDM.

[6]  Kristin P. Bennett,et al.  Duality and Geometry in SVM Classifiers , 2000, ICML.

[7]  Kongqiao Wang,et al.  Active learning for image retrieval with Co-SVM , 2007, Pattern Recognit..

[8]  Cheung-Chi Leung,et al.  Comparison of Speaker Adaptation Methods as Feature Extraction for SVM-Based Speaker Recognition , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[9]  S. Sathiya Keerthi,et al.  A fast iterative nearest point algorithm for support vector machine classifier design , 2000, IEEE Trans. Neural Networks Learn. Syst..

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

[11]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[12]  Leon N. Cooper,et al.  Training Data Selection for Support Vector Machines , 2005, ICNC.

[13]  Nikos Koutsias,et al.  SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Yong Shi,et al.  Subspace Distance-Based Sampling Method for SVM , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[15]  Ming-Syan Chen,et al.  On the Design and Analysis of the Privacy-Preserving SVM Classifier , 2011, IEEE Transactions on Knowledge and Data Engineering.

[16]  Antônio de Pádua Braga,et al.  SVM-KM: speeding SVMs learning with a priori cluster selection and k-means , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.

[17]  Volker Blanz,et al.  Component-Based Face Recognition with 3D Morphable Models , 2004, CVPR Workshops.

[18]  Xiaofei Zhou,et al.  Kernel subclass convex hull sample selection method for SVM on face recognition , 2010, Neurocomputing.

[19]  Volker Blanz,et al.  Component-Based Face Recognition with 3D Morphable Models , 2003, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[20]  Sungzoon Cho,et al.  Invariance of neighborhood relation under input space to feature space mapping , 2005, Pattern Recognit. Lett..

[21]  Su-Yun Huang,et al.  Reduced Support Vector Machines: A Statistical Theory , 2007, IEEE Transactions on Neural Networks.

[22]  Thorsten Joachims,et al.  Detecting Concept Drift with Support Vector Machines , 2000, ICML.