Multi-Graph-View Learning for Complicated Object Classification

In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to explore subgraph features from multiple graphviews for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.

[1]  James Ze Wang,et al.  Real-time computerized annotation of pictures. , 2008, IEEE transactions on pattern analysis and machine intelligence.

[2]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[3]  Xin Xu,et al.  Logistic Regression and Boosting for Labeled Bags of Instances , 2004, PAKDD.

[4]  Zhi-Hua Zhou.  Multi-Instance Learning : A Survey , 2004 .

[5]  Charu C. Aggarwal,et al.  Evolutionary Network Analysis , 2014, ACM Comput. Surv..

[6]  S. Nash,et al.  Linear and Nonlinear Programming , 1987 .

[7]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[9]  Philip S. Yu,et al.  Brain network analysis: a data mining perspective , 2014, SKDD.

[10]  Michael Mayo,et al.  Experiments with multi-view multi-instance learning for supervised image classification , 2011 .

[11]  José Eladio Medina-Pagola,et al.  A new proposal for graph-based image classification using frequent approximate subgraphs , 2014, Pattern Recognit..

[12]  Philip S. Yu,et al.  Bag Constrained Structure Pattern Mining for Multi-Graph Classification , 2014, IEEE Transactions on Knowledge and Data Engineering.

[13]  Zaïd Harchaoui,et al.  Image Classification with Segmentation Graph Kernels , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Xue Chen,et al.  Building Association Link Network for Semantic Link on Web Resources , 2011, IEEE Transactions on Automation Science and Engineering.

[15]  Chengqi Zhang,et al.  Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification , 2015, IEEE Transactions on Cybernetics.

[16]  Chengqi Zhang,et al.  Multi-graph-view Learning for Graph Classification , 2014, 2014 IEEE International Conference on Data Mining.

[17]  Matus Telgarsky,et al.  A Primal-Dual Convergence Analysis of Boosting , 2011, J. Mach. Learn. Res..

[18]  Chengqi Zhang,et al.  Multi-Graph Learning with Positive and Unlabeled Bags , 2014, SDM.

[19]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[20]  James Ze Wang,et al.  Real-Time Computerized Annotation of Pictures , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.