Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Machine Learning
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd 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
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Web-scale computer vision using MapReduce for multimedia data mining
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Large-scale multimodal mining for healthcare with mapreduce
Proceedings of the 1st ACM International Health Informatics Symposium
Semantic analysis and retrieval in personal and social photo collections
Multimedia Tools and Applications
Concept modeling: From origins to multimedia
Multimedia Tools and Applications
Lookapp: interactive construction of web-based concept detectors
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Multimedia Applications and Security in MapReduce: Opportunities and Challenges
Concurrency and Computation: Practice & Experience
Riding the multimedia big data wave
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Massive-scale multimedia semantic modeling
Proceedings of the 21st ACM international conference on Multimedia
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With the rapid growth of multimedia data, it becomes increasingly important to develop semantic concept modeling approaches that are consistently effective, highly efficient, and easily scalable. To this end, we first propose the robust subspace bagging (RB-SBag) algorithm by augmenting random subspace bagging with forward model selection. Compared with traditional modeling approaches, RB-SBag offers a considerably faster learning process while minimizing the risk of overfitting. Its ensemble structure also enables a convenient transformation into a simple parallel framework called MapReduce. To further improve scalability, we also develop a task scheduling algorithm to optimize task placement for heterogenous tasks. On a collection consisting of more than 250,000 images and several standard TRECVID benchmark datasets, RB-SBag achieved more than a 10-fold speedup with comparable or even better classification performance than baseline SVMs. We also deployed the MapReduce implementation on a 16-node Hadoop cluster, where the proposed task scheduler demonstrates a significantly better scalability than the baseline scheduler in the presence of task heterogeneity.