Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Machine Learning - Special issue on inductive transfer
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Usage patterns of collaborative tagging systems
Journal of Information Science
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Generalized component analysis for text with heterogeneous attributes
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Social and semantics analysis via non-negative matrix factorization
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Annotating images by harnessing worldwide user-tagged photos
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Search Results Clustering Using Nonnegative Matrix Factorization (NMF)
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
A bayesian framework for learning shared and individual subspaces from multiple data sources
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
Triplex transfer learning: exploiting both shared and distinct concepts for text classification
Proceedings of the sixth ACM international conference on Web search and data mining
Connecting comments and tags: improved modeling of social tagging systems
Proceedings of the sixth ACM international conference on Web search and data mining
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Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.