Name
Abstract | ||
|---|---|---|
Label distribution learning (LDL) assumes labels are associated with each instance to some degree and tries to model the relationship between labels and instances. LDL has achieved great success in many applications, but most existing LDL methods are designed for data with complete annotation information. However, in reality, supervised information often be incomplete due to the huge costs of data collection. In this paper, we propose a novel incomplete label distribution learning method based on supervised neighborhood information (IncomLDL-SNI). The proposed method uses partial least squares to project the original data into a supervised feature space where instances with similar labels are likely to be projected together. Then, IncomLDL-SNI utilizes the Euclidean distance to find the nearest neighbors for target samples in the supervised feature space and recovers the missing annotations from the neighborhood label Information. Extensive experiments on various data sets validate the effectiveness of our proposal. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s13042-019-00958-x | International Journal of Machine Learning and Cybernetics |
Keywords | Field | DocType |
Label distribution learning, Incomplete annotation, Partial least squares, Supervised neighborhood information | Data collection,Data set,Feature vector,Annotation,Pattern recognition,Computer science,Partial least squares regression,Euclidean distance,Artificial intelligence | Journal |
Volume | Issue | ISSN |
11 | 1 | 1868-8071 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-qiang Zeng | 1 | 76 | 7.91 |
| Sufen Chen | 2 | 17 | 3.66 |
| Run Xiang | 3 | 1 | 0.68 |
| Guo-Zheng Li | 4 | 368 | 42.62 |
| Xue-Feng Fu | 5 | 1 | 0.35 |