Name
Abstract | ||
|---|---|---|
•In this paper, we study the embedding of labels together with the group information with an objective to build an efficient multi-label classification.•We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded.•We ensure that labels belonging to the same group share the same sparsity pattern in their low-rank representations.•The proposed method has three major stages namely (1) Identification of groups of labels; (2) Sparsity-invariant embedding of label groups; and (3) Embedding of feature matrix to the same low-rank space.•Extensive comparative studies validate the effectiveness of the proposed method against the state-of-the-art multi-label learning approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.01.009 | Pattern Recognition |
Keywords | DocType | Volume |
Multi-label classification,Label embedding,Matrix factorization | Journal | 90 |
Issue | ISSN | Citations |
1 | 0031-3203 | 5 |
PageRank | References | Authors |
0.42 | 28 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vikas Kumar 0003 | 1 | 25 | 4.76 |
| Arun K. Pujari | 2 | 420 | 48.20 |
| Vineet Padmanabhan | 3 | 216 | 25.90 |
| Venkateswara Rao Kagita | 4 | 59 | 8.13 |