Constrained Local and Global Consistency for semi-supervised learning

One of the widely used algorithms for graph-based semi-supervised learning (SSL) is the Local and Global Consistency (LGC). Such an algorithm can be viewed as a convex optimization problem that balances fitness on labeled examples and smoothness on the graph using a graph Laplacian. In this paper, we provide a novel graph-based SSL algorithm incorporating two normalization constraints into the regularization framework of LGC. We prove that our method has closed-form solution and generalizes two existing methods, being more flexible than the original ones. Through experiments on benchmark data sets, we show the effectiveness of our method, which consistently outperforms the competing methods.

[1]  Celso André R. de Sousa,et al.  Influence of Graph Construction on Semi-supervised Learning , 2013, ECML/PKDD.

[2]  Tong Zhang,et al.  On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning , 2007, J. Mach. Learn. Res..

[3]  Wei Liu,et al.  Large Graph Construction for Scalable Semi-Supervised Learning , 2010, ICML.

[4]  Wei Liu,et al.  Robust multi-class transductive learning with graphs , 2009, CVPR.

[5]  Celso André R. de Sousa,et al.  An inductive semi-supervised learning approach for the Local and Global Consistency algorithm , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[6]  Bernhard Schölkopf,et al.  Introduction to Semi-Supervised Learning , 2006, Semi-Supervised Learning.

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

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

[9]  Celso Andre Rodrigues de Sousa,et al.  Impacto da geração de grafos na classificação semissupervisionada , 2013 .

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

[11]  Gustavo E. A. P. A. Batista,et al.  Robust Multi-class Graph Transduction with higher order regularization , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[12]  James T. Kwok,et al.  Prototype vector machine for large scale semi-supervised learning , 2009, ICML '09.

[13]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[14]  Celso André R. de Sousa,et al.  An overview on the Gaussian Fields and Harmonic Functions method for semi-supervised learning , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[15]  Mikhail Belkin,et al.  Semi-supervised Learning by Higher Order Regularization , 2011, AISTATS.

[16]  Celso André R. de Sousa,et al.  An experimental analysis on time series transductive classification on graphs , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[17]  Shih-Fu Chang,et al.  Graph construction and b-matching for semi-supervised learning , 2009, ICML '09.

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

[19]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[20]  Ulrike von Luxburg,et al.  Graph Laplacians and their Convergence on Random Neighborhood Graphs , 2006, J. Mach. Learn. Res..

[21]  Celso André R. de Sousa,et al.  Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study , 2014, 2014 22nd International Conference on Pattern Recognition.