Twin Support Vector Machine With Local Structural Information for Pattern Classification

Many versions of support vector machine with structural information exploit the useful prior knowledge to directly improve the algorithm’s generalization. The prior knowledge embodies the structure of data, but it cannot fully reflect the local nonlinear structure of data. In this paper, a twin support vector machine with local structural information (LSI-TSVM) is proposed. The LSI-TSVM embeds the local within-class and between-class distribution information of data, which makes it contain not only the original global within-class clustering and between-class margin but also the local within-class and between-class scatters. Furthermore, our LSI-TSVM is extended to a nonlinear version with a kernel trick. All experiments show that our LSI-TSVM is superior to the state-of-the-art algorithms in a generalization performance.

[1]  Hongyuan Zha,et al.  Entropy-based fuzzy support vector machine for imbalanced datasets , 2017, Knowl. Based Syst..

[2]  Zhi-Hua Zhou,et al.  Large margin distribution machine , 2013, KDD.

[3]  Hamid Reza Shahdoosti,et al.  Combined ripplet and total variation image denoising methods using twin support vector machines , 2018, Multimedia Tools and Applications.

[4]  Qiang Yang,et al.  Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier , 2011, IEEE Transactions on Neural Networks.

[5]  Yong Shi,et al.  ν-Nonparallel support vector machine for pattern classification , 2014, Neural Computing and Applications.

[6]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Nai-Yang Deng,et al.  Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions , 2012 .

[9]  Olvi L. Mangasarian,et al.  Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Muhammad Tanveer,et al.  EEG signal classification using universum support vector machine , 2018, Expert Syst. Appl..

[11]  Yong Shi,et al.  Structural twin support vector machine for classification , 2013, Knowl. Based Syst..

[12]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[13]  Hakan Cevikalp,et al.  Best Fitting Hyperplanes for Classification , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Xianli Pan,et al.  A Novel Twin Support-Vector Machine With Pinball Loss , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Mangui Liang,et al.  Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises , 2013, Neurocomputing.

[17]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

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

[19]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[20]  Liming Liu,et al.  Multi-class classification method using twin support vector machines with multi-information for steel surface defects , 2018 .

[21]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[23]  Li Zhang,et al.  Fisher-regularized support vector machine , 2016, Inf. Sci..

[24]  Xiaohui Liu,et al.  Structural nonparallel support vector machine for pattern recognition , 2016, Pattern Recognit..

[25]  Yuan-Hai Shao,et al.  Improvements on Twin Support Vector Machines , 2011, IEEE Transactions on Neural Networks.

[26]  Xianli Pan,et al.  K-nearest neighbor based structural twin support vector machine , 2015, Knowl. Based Syst..

[27]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

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

[29]  Na Zhang,et al.  Extended least squares support vector machines for ordinal regression , 2015, Neural Computing and Applications.

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

[31]  Dong Xu,et al.  Structural regularized projection twin support vector machine for data classification , 2014, Inf. Sci..

[32]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[33]  Nan Zhang,et al.  Twin support vector machine: theory, algorithm and applications , 2017, Neural Computing and Applications.

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

[35]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[36]  Johan A. K. Suykens,et al.  Support Vector Machine Classifier With Pinball Loss , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Jian Yang,et al.  Recursive projection twin support vector machine via within-class variance minimization , 2011, Pattern Recognit..

[38]  Shu-Cherng Fang,et al.  FUZZY QUADRATIC SURFACE SUPPORT VECTOR MACHINE BASED ON FISHER DISCRIMINANT ANALYSIS , 2015 .

[39]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[40]  Daniel S. Yeung,et al.  Structured large margin machines: sensitive to data distributions , 2007, Machine Learning.