Abstract
In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming popular in domain specific learning tasks, aggregating multiple feature views to represent multi-instance bags has recently shown promising results, mainly because multiple views provide extra information for MIL tasks. Nevertheless, multiple views also increase the risk of involving redundant views and irrelevant features for learning. In this paper, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL. To achieve the goal, we design a new optimization problem by integrating both multi-view representation and multi-instance bag constraints. The solution to the objective function will ensure that the identified top-m features are the most informative ones across all feature views. Experiments on two real-world applications demonstrate the performance of the cross-view feature selection for content-based image retrieval and social media content recommendation.
Original language | English |
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Title of host publication | CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1699-1708 |
Number of pages | 10 |
ISBN (Electronic) | 9781450325981 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China Duration: 3 Nov 2014 → 7 Nov 2014 |
Other
Other | 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 |
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Country/Territory | China |
City | Shanghai |
Period | 3/11/14 → 7/11/14 |
Keywords
- multi-instance learning
- cross-view feature selection
- Cross-view feature selection
- Multi-instance learning