The Journal of Machine Learning Research
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
Pfp: parallel fp-growth for query recommendation
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
A decentralized approach for mining event correlations in distributed system monitoring
Journal of Parallel and Distributed Computing
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The amount of online photos and videos is now at the scale of tens of billions. To organize, index, and retrieve these large-scale rich-media data, a system must employ scalable data management and mining algorithms. The research community needs to consider solving large scale problems rather than solving problems with small datasets that do not reflect real life scenarios. This tutorial introduces key challenges in large-scale rich-media data mining, and presents parallel algorithms for tackling such challenges. We present our parallel implementations of Spectral Clustering (PSC), FP-Growth (PFP), Latent Dirichlet Allocation (PLDA), and Support Vector Machines (PSVM).