Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Fast protein classification with multiple networks
Bioinformatics
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
A coordinate gradient descent method for nonsmooth separable minimization
Mathematical Programming: Series A and B
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance metric learning from uncertain side information with application to automated photo tagging
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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For large scale image data mining, a challenging problem is to design a method that could work efficiently under the situation of little ground-truth annotation and a mass of unlabeled or noisy data. As one of the major solutions, semi-supervised learning (SSL) has been deeply investigated and widely used in image classification, ranking and retrieval. However, most SSL approaches are not able to incorporate multiple information sources. Furthermore, no sample selection is done on unlabeled data, leading to the unpredictable risk brought by uncontrolled unlabeled data and heavy computational burden that is not suitable for learning on real world dataset. In this paper, we propose a scalable semi-supervised multiple kernel learning method (S3MKL) to deal with the first problem. Our method imposes group LASSO regularization on the kernel coefficients to avoid over-fitting and conditional expectation consensus for regularizing the behaviors of different kernel on the unlabeled data. To reduce the risk of using unlabeled data, we also design a hashing system where multiple kernel locality sensitive hashing (MKLSH) are constructed with respect to different kernels to identify a set of "informative" and "compact" unlabeled training subset from a large unlabeled data corpus. Combining S3MKL with MKLSH, the method is suitable for real world image classification and personalized web image re-ranking with very little user interaction. Comprehensive experiments are conducted to test the performance of our method, and the results show that our method provides promising powers for large scale real world image classification and retrieval.