Name
Abstract | ||
|---|---|---|
•Multi-label learning deals with the classification of data with multiple labels.•Output space with many labels is tackle by modeling inter-label correlations.•Use of parametrization and embedding have been the prime focus.•A piecewise-linear embedding using maximum margin matrix factorization is proposed.•Our experimental analysis manifests the superiority of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2017.09.020 | Expert Systems with Applications |
Keywords | Field | DocType |
Multi-label learning,Matrix factorization,Label correlation | Prime (order theory),Data mining,Computer science,Multi-label classification,Artificial intelligence,Feature vector,Automatic image annotation,Embedding,Pattern recognition,Sentiment analysis,Matrix decomposition,Hierarchical matrix,Machine learning | Journal |
Volume | Issue | ISSN |
91 | C | 0957-4174 |
Citations | PageRank | References |
3 | 0.37 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vikas Kumar 0003 | 1 | 25 | 4.76 |
| Arun K. Pujari | 2 | 420 | 48.20 |
| Vineet Padmanabhan | 3 | 216 | 25.90 |
| Sandeep Kumar Sahu | 4 | 19 | 2.63 |
| Venkateswara Rao Kagita | 5 | 59 | 8.13 |