R
Rong Jin
Researcher at Alibaba Group
Publications - 458
Citations - 21966
Rong Jin is an academic researcher from Alibaba Group. The author has contributed to research in topics: Image retrieval & Cluster analysis. The author has an hindex of 75, co-authored 449 publications receiving 19456 citations. Previous affiliations of Rong Jin include University of Pittsburgh & Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
Understanding bag-of-words model: A statistical framework
Yin Zhang,Rong Jin,Zhi-Hua Zhou +2 more
TL;DR: A statistical framework which generalizes the bag-of-words representation, in which the visual words are generated by a statistical process rather than using a clustering algorithm, while the empirical performance is competitive to clustering-based method.
Proceedings Article
Active Learning by Querying Informative and Representative Examples
TL;DR: The proposed QUIRE approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance by incorporating the correlation among labels and is extended to multi-label learning by actively querying instance-label pairs.
Proceedings ArticleDOI
Batch mode active learning and its application to medical image classification
TL;DR: A framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously and is more effective than the state-of-the-art algorithms for active learning.
Proceedings ArticleDOI
Combining link and content for community detection: a discriminative approach
TL;DR: A discriminative model for combining the link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web is proposed and introduced and hidden variables are introduced to explicitly model the popularity of nodes.
Proceedings Article
Learning with Multiple Labels
Rong Jin,Zoubin Ghahramani +1 more
TL;DR: This paper proposes a novel discriminative approach for handling the ambiguity of class labels in the training examples and shows that the approach is able to find the correct label among the set of candidate labels and actually achieve performance close to the case when each training instance is given a single correct label.