Name
Abstract | ||
|---|---|---|
Hashing techniques have been extensively investigated to boost similarity search for large-scale high-dimensional data. Most of the existing approaches formulate the their objective as a pair-wise similarity-preserving problem. In this paper, we consider the hashing problem from the perspective of optimizing a list-wise learning to rank problem and propose an approach called List-Wise supervised Hashing (LWH). In LWH, the hash functions are optimized by employing structural SVM in order to explicitly minimize the ranking loss of the whole list-wise permutations instead of merely the point-wise or pair-wise supervision. We evaluate the performance of LWH on two real-world data sets. Experimental results demonstrate that our method obtains a significant improvement over the state-of-the-art hashing approaches due to both structural large margin and list-wise ranking pursuing in a supervised manner. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2600428.2609494 | SIGIR |
Keywords | Field | DocType |
hashing,information search and retrieval,learning to rank,structural svm | Locality-sensitive hashing,Data mining,Learning to rank,Computer science,Universal hashing,Feature hashing,K-independent hashing,Hash function,Artificial intelligence,Machine learning,Dynamic perfect hashing,Nearest neighbor search | Conference |
Citations | PageRank | References |
4 | 0.38 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou Yu | 1 | 278 | 39.88 |
| Fei Wu | 2 | 2209 | 153.88 |
| Yin Zhang | 3 | 3492 | 281.04 |
| Siliang Tang | 4 | 179 | 33.98 |
| Jian Shao | 5 | 261 | 21.83 |
| Yue-Ting Zhuang | 6 | 3549 | 216.06 |