Nonnegative shared subspace learning and its application to social media retrieval
By Sunil Gupta, Dinh Q. Phung, Brett Adams, Truyen Tran, Svetha Venkatesh. 2010
105 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mingsheng Long
- Jianmin Wang
- Qiang Yang
- Gabriella Csurka
- Tao Mei
- Kate Saenko
- Guiguang Ding
- Dongrui Wu
In Computer Vision and Pattern Recognition
Citations in the corpus
- Deep Collaborative Embedding for Social Image Understanding (2019, background): “A shared space between two sources is learned under NMF by jointly capturing the shared basis vectors and their individual bases in [50].”
- Transfer Learning with Graph Co-Regularization (2012, background): “Collective Matrix Factorization (CMF) (Singh and Gordon 2008) and its tri-factorization variants have been extensively studied for transfer learning recently (Gupta et al. 2010; Long et al. 2010; Wang et al. 2011; Zhu et al. 2011; Zhuang et al. 2011; Long et al. 2012).”