Learning with limited and noisy tagging

With the rapid development of social networks, tagging has become an important means responsible for such rapid development. A robust tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called SpSVM-MC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label constraints into the optimization. While SpSVM-MC is a general method for learning with limited and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Theoretical analysis and extensive evaluations in comparison with state-of-the-art literature demonstrate that SpSVM-MC outstands with a superior performance.

[1]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[2]  Xingquan Zhu,et al.  Class Noise vs. Attribute Noise: A Quantitative Study , 2003, Artificial Intelligence Review.

[3]  Zenglin Xu,et al.  Efficient Convex Relaxation for Transductive Support Vector Machine , 2007, NIPS.

[4]  Edward Y. Chang,et al.  SVM binary classifier ensembles for image classification , 2001, CIKM '01.

[5]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[6]  Dong Liu,et al.  Image retagging , 2010, ACM Multimedia.

[7]  Yi Liu,et al.  Soft SVM and Its Application in Video-Object Extraction , 2007, IEEE Transactions on Signal Processing.

[8]  Xindong Wu,et al.  Eliminating Class Noise in Large Datasets , 2003, ICML.

[9]  Sunita Sarawagi,et al.  Discriminative Methods for Multi-labeled Classification , 2004, PAKDD.

[10]  M. Wand,et al.  Semiparametric Regression: Parametric Regression , 2003 .

[11]  Zhi-Hua Zhou,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2006, NIPS.

[12]  Thomas Hofmann,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2007 .

[13]  Xiaojun Qi,et al.  Incorporating multiple SVMs for automatic image annotation , 2007, Pattern Recognit..

[14]  Edward Y. Chang,et al.  Semiparametric Regression Using Student $t$ Processes , 2007, IEEE Transactions on Neural Networks.

[15]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[16]  Jing Hua,et al.  Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Christos Faloutsos,et al.  Semi-Supervised Learning Based on Semiparametric Regularization , 2008, SDM.

[18]  Robert Tibshirani,et al.  1-norm Support Vector Machines , 2003, NIPS.

[19]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[20]  Tommi S. Jaakkola,et al.  Information Regularization with Partially Labeled Data , 2002, NIPS.

[21]  Ayhan Demiriz,et al.  Semi-Supervised Support Vector Machines , 1998, NIPS.

[22]  Zhe Sun,et al.  Manifold regularization based semisupervised semiparametric regression , 2010, Neurocomputing.

[23]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[24]  Ramesh C. Jain,et al.  Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images , 2011, TIST.

[25]  Shuicheng Yan,et al.  Image tag refinement towards low-rank, content-tag prior and error sparsity , 2010, ACM Multimedia.

[26]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[27]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[28]  Taghi M. Khoshgoftaar,et al.  Class noise detection using frequent itemsets , 2006, Intell. Data Anal..

[29]  Venkateswara Rao,et al.  Adaptive Kernel-Based Image Denoising Employing Semi-Parametric Regularization , 2012 .

[30]  Changhu Wang,et al.  Content-Based Image Annotation Refinement , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.