An equivalence between sparse approximation and support vector machines
Neural Computation
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Automatic video annotation by semi-supervised learning with kernel density estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Uncovering shared structures in multiclass classification
Proceedings of the 24th international conference on Machine learning
Kodak's consumer video benchmark data set: concept definition and annotation
Proceedings of the international workshop on Workshop on multimedia information retrieval
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Multi-label boosting for image annotation by structural grouping sparsity
Proceedings of the international conference on Multimedia
Heterogeneous feature selection by group lasso with logistic regression
Proceedings of the international conference on Multimedia
Image annotation by composite kernel learning with group structure
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations
IEEE Transactions on Multimedia
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
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With the explosive growth of multimedia data in the web, multi-label image annotation has been attracted more and more attention. Although the amount of available data is large and growing, the number of labeled data is quite small. This paper proposes an approach to utilize both unlabeled data in target domain and labeled data in auxiliary domain to boost the performance of image annotation. Moreover, since different kinds of heterogeneous features in images have different intrinsic discriminative power for image understanding, group sparsity is introduced in our approach to effectively utilize those heterogeneous visual features with data of target and auxiliary domains. We call this approach semi-supervised cross-domain learning with group sparsity (S^2CLGS). The strength of the proposed S^2CLGS method for multi-label image annotation is to integrate semi-supervised discriminant analysis, cross-domain learning and sparse coding together. Experiments demonstrate the effectiveness of S^2CLGS in comparison with other image annotation algorithms.