Name
Abstract | ||
|---|---|---|
Vector data containing direction and magnitude information other than position information is different from common point data only containing position information. Those general similarity measures for point data such as Euclidean distance are not suitable for vector data. Thus, a novel measure must be proposed to estimate the similarity between vectors. The similarity measure defined in this paper combines Euclidean distance with angle and magnitude differences. Based on this measure, we construct a vector field space on which a modified locally linear embedding (LLE) algorithm is used for vector field learning. Our experimental results show that the proposed similarity measure works better than traditional Euclidean distance. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11759966_65 | ISNN (1) |
Keywords | Field | DocType |
euclidean distance,novel measure,general similarity measure,vector field learning,common point data,position information,similarity measure,point data,vector data,proposed similarity measure,vector field | Pattern recognition,Transverse measure,Similarity measure,Vector measure,Direction vector,Similarity (network science),Euclidean distance,Artificial intelligence,Distance from a point to a line,Direction cosine,Mathematics | Conference |
Volume | ISSN | ISBN |
3971 | 0302-9743 | 3-540-34439-X |
Citations | PageRank | References |
4 | 0.64 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyu Li | 1 | 443 | 32.34 |
| I-Fan Shen | 2 | 173 | 12.32 |