Name
Title | ||
|---|---|---|
Evolutionary Distance Metric Learning Approach to Semi-supervised Clustering with Neighbor Relations |
Abstract | ||
|---|---|---|
This study proposes a distance metric learning method based on a clustering index with neighbor relation that simultaneously evaluates inter-and intra-clusters. Our proposed method optimizes a distance transform matrix based on the Mahalanobis distance by utilizing a self-adaptive differential evolution (jDE) algorithm. Our approach directly improves various clustering indices and in principle requires less auxiliary information compared to conventional metric learning methods. We experimentally validated the search efficiency of jDE and the generalization performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICTAI.2013.66 | ICTAI |
Keywords | Field | DocType |
semi-supervised clustering,neighbor relations,inter-and intra-clusters,mahalanobis distance,generalization performance,various clustering index,clustering index,neighbor relation,conventional metric learning method,auxiliary information,distance metric learning method,evolutionary distance metric learning,learning artificial intelligence,evolutionary computation | k-medians clustering,Hierarchical clustering,Fuzzy clustering,Correlation clustering,Pattern recognition,Computer science,Metric (mathematics),Mahalanobis distance,Artificial intelligence,Cluster analysis,Machine learning,Single-linkage clustering | Conference |
ISSN | Citations | PageRank |
1082-3409 | 5 | 0.46 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ken-ichi Fukui | 1 | 24 | 9.74 |
| Satoshi Ono | 2 | 219 | 39.83 |
| Taishi Megano | 3 | 8 | 1.19 |
| Masayuki Numao | 4 | 390 | 89.56 |