Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Semi-supervised learning by mixed label propagation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Boosting Clustering by Active Constraint Selection
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Improving constrained clustering with active query selection
Pattern Recognition
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In this position paper, we first review the state-of-the-art in graph-based semi-supervised learning, and point out three limitations that are particularly relevant to multimedia analysis: (1) rich data is restricted to live on a single manifold; (2) learning must happen in batch mode; and (3) the target label is assumed smooth on the manifold. We then discuss new directions in semi-supervised learning research that can potentially overcome these limitations: (i) modeling data as a mixture of multiple manifolds that may intersect or overlap; (ii) online semi-supervised learning that learns incrementally with low computation and memory needs; and (iii) learning spectrally sparse but non-smooth labels with compressive sensing. We give concrete examples in each new direction. We hope this article will inspire new research that makes semi-supervised learning an even more valuable tool for multimedia analysis.