Semi-supervised Learning Using Local Regularizer and Unit Circle Class Label Representation

Semi-supervised learning, which aims to learn from partially labeled data and mostly unlabeled data, has been attracted more and more attention in machine learning and pattern recognition. A novel semi-supervised classification approach is proposed, which can not only handle semi-supervised binary classification problem but also deal with semi-supervised multi-class classification problem. The approach is based on local regularizer and unit circle class label representation. The former is minimized so as to cause the class labels to have the desired properties. The latter utilizes two-dimensional vector evenly distributed in circumference of unit circle to represent class label, so multi-class classification can be performed only once. Comparative classification experiments on some benchmark datasets validate the effectiveness of the presented approach.

[1]  Shiliang Sun,et al.  Local within-class accuracies for weighting individual outputs in multiple classifier systems , 2010, Pattern Recognit. Lett..

[2]  Hui Xu,et al.  Two-dimensional supervised local similarity and diversity projection , 2010, Pattern Recognit..

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

[4]  Feiping Nie,et al.  Semi-Supervised Classification via Local Spline Regression , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Fei Wang,et al.  A general learning framework using local and global regularization , 2010, Pattern Recognit..

[6]  Léon Bottou,et al.  Local Algorithms for Pattern Recognition and Dependencies Estimation , 1993, Neural Computation.

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

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

[9]  Rong Jin,et al.  Efficient Algorithm for Localized Support Vector Machine , 2010, IEEE Transactions on Knowledge and Data Engineering.

[10]  Bernhard Schölkopf,et al.  Transductive Classification via Local Learning Regularization , 2007, AISTATS.

[11]  Mikhail Belkin,et al.  Tikhonov regularization and semi-supervised learning on large graphs , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[13]  Gilles Blanchard,et al.  Semi-Supervised Novelty Detection , 2010, J. Mach. Learn. Res..

[14]  Fei Wang,et al.  Label Propagation through Linear Neighborhoods , 2006, IEEE Transactions on Knowledge and Data Engineering.

[15]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[16]  Jiangtao Peng,et al.  Semi-supervised learning based on high density region estimation , 2010, Neural Networks.

[17]  Antonio Torralba,et al.  Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Léon Bottou,et al.  Local Learning Algorithms , 1992, Neural Computation.

[19]  Gang Wang,et al.  Semi-supervised Classification Using Local and Global Regularization , 2008, AAAI.

[20]  Lv Jia.  Realization of Text Classification System Based on Improved Classification Model , 2009 .

[21]  Carlos Eduardo Pedreira,et al.  A new local-global approach for classification , 2010, Neural Networks.

[22]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[23]  Zhang Chun-ping,et al.  Research on K-means Clustering Algorithm , 2011 .

[24]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .