Name
Title | ||
|---|---|---|
Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels |
Abstract | ||
|---|---|---|
Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space. As a consequence, the multi-label learning process can be augmented significantly by exploiting the label correlations effectively. Most of the existing approaches share the limitations in that the label correlations are typically taken as prior knowledge, which may not depict the true dependencies among labels correctly, or they do not adequately address the issue of missing labels. In this paper, we propose an integrated framework that learns the correlations among labels while training the multi-label model simultaneously. Specifically, a low rank structure is adopted to capture the complex correlations among labels. In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations. An alternating algorithm is then developed to solve the optimization problem. Extensive experiments are conducted on a number of image and text data sets to demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDM.2014.125 | ICDM |
Keywords | Field | DocType |
optimisation,learning (artificial intelligence),semantic space,matrix algebra,multi-label learning,multilabel classification,low-rank label correlation learning,optimization problem,image classification,supplementary label matrix,alternating algorithm,label correlation,text data sets,multilabel learning,text analysis,image data sets,low rank,missing labels,semantics,vectors,correlation | Data mining,Data set,Computer science,Matrix (mathematics),Multi-label classification,Artificial intelligence,Optimization problem,Pattern recognition,Automatic label placement,Correlation,Semantics,Machine learning,Semantic space | Conference |
ISSN | Citations | PageRank |
1550-4786 | 22 | 0.66 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linli Xu | 1 | 790 | 42.51 |
| Zhen Wang | 2 | 45 | 15.47 |
| Zefan Shen | 3 | 22 | 0.66 |
| Yubo Wang | 4 | 24 | 1.00 |
| Enhong Chen | 5 | 1235 | 86.93 |