Name
Abstract | ||
|---|---|---|
The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for single-view problems. In fact, in many real-world applications, examples are represented by multiple sets of features. In this paper, we extend the idea of local learning to multi-view problem, design a multi-view local model for each example, and propose a Multi-View Local Learning Regularization (MVLL-Reg) matrix. Both its linear and kernel version are given. Experiments are conducted to demonstrate the superiority of the proposed method over several state-of-the-art ones. |
Year | Venue | Keywords |
|---|---|---|
2008 | AAAI | local learning method,multiple set,multi-view local learning,local learning,real-world application,multi-view local learning regularization,multi-view local model,kernel version,clustering problem,particular example |
Field | DocType | Citations |
Kernel (linear algebra),Semi-supervised learning,Instance-based learning,Stability (learning theory),Active learning (machine learning),Computer science,Unsupervised learning,Regularization (mathematics),Artificial intelligence,Cluster analysis,Machine learning | Conference | 13 |
PageRank | References | Authors |
0.74 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Zhang | 1 | 461 | 22.17 |
| Fei Wang | 2 | 2139 | 135.03 |
| Changshui Zhang | 3 | 5506 | 323.40 |
| Tao Li | 4 | 7216 | 393.45 |