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Content Provider | ACM Digital Library |
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Author | Wang, Qifan Ruan, Lingyun Zhang, Zhiwei Si, Luo |
Abstract | Tags have been popularly utilized in many applications with image and text data for better managing, organizing and searching for useful information. Tag completion provides missing tag information for a set of existing images or text documents while tag prediction recommends tag information for any new image or text document. Valuable prior research has focused on improving the accuracy of tag completion and prediction, but limited research has been conducted for the efficiency issue in tag completion and prediction, which is a critical problem in many large scale real world applications. This paper proposes a novel efficient Hashing approach for Tag Completion and Prediction (HashTCP). In particular, we construct compact hashing codes for both data examples and tags such that the observed tags are consistent with the constructed hashing codes and the similarities between data examples are also preserved. We then formulate the problem of learning binary hashing codes as a discrete optimization problem. An efficient coordinate descent method is developed as the optimization procedure for the relaxation problem. A novel binarization method based on orthogonal transformation is proposed to obtain the binary codes from the relaxed solution. Experimental results on four datasets demonstrate that the proposed approach can achieve similar or even better accuracy with state-of-the-art methods and can be much more efficient, which is important for large scale applications. |
Starting Page | 1789 |
Ending Page | 1794 |
Page Count | 6 |
File Format | |
ISBN | 9781450322638 |
DOI | 10.1145/2505515.2505649 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-27 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Hashing Multi-label learning Tag completion |
Content Type | Text |
Resource Type | Article |
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