Abstract
We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of 'predictability'. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.
Original language | English |
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Pages | 2365-2373 |
Number of pages | 9 |
Publication status | Published - 2013 |
Event | 30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States Duration: 2013 Jun 16 → 2013 Jun 21 |
Conference
Conference | 30th International Conference on Machine Learning, ICML 2013 |
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Country/Territory | United States |
City | Atlanta, GA |
Period | 13/6/16 → 13/6/21 |
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Sociology and Political Science