Name
Abstract | ||
|---|---|---|
Multi-view tagging has become increasingly popular in the applications where data representations by multiple views exist. A robust multi-view tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called MSMC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label space consistency into the optimization. While MSMC is a general method for learning with multi-view, limited, and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Extensive evaluations in comparison with state-of-the-art literature demonstrate that MSMC outstands with a superior performance. |
Year | Venue | Field |
|---|---|---|
2016 | IJCAI | Pattern recognition,Computer science,Exploit,Regularization (mathematics),Artificial intelligence,Discriminative model,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingming Li | 1 | 57 | 14.82 |
| Ming Yang | 2 | 54 | 5.64 |
| Zenglin Xu | 3 | 923 | 66.28 |
| Zhongfei (Mark) Zhang | 4 | 2451 | 164.30 |