Name
Title | ||
|---|---|---|
Informative Energy Metric For Similarity Measure In Reproducing Kernel Hilbert Spaces |
Abstract | ||
|---|---|---|
In this paper, information energy metric (IEM) is obtained by similarity computing for high-dimensional samples in a reproducing kernel Hilbert space (RKHS). Firstly, similar/dissimilar subsets and their corresponding informative energy functions are defined. Secondly, IEM is proposed for similarity measure of those subsets, which converts the non-metric distances into metric ones. Finally, applications of this metric is introduced, such as classification problems. Experimental results validate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1080/18756891.2012.670530 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS |
Keywords | Field | DocType |
Kernel methods, Similarity measure, Reproducing kernel Hilbert space, Non-metric distance | Similarity measure,Intrinsic metric,Metric (mathematics),Kernel principal component analysis,Artificial intelligence,Discrete mathematics,Fisher information metric,Kernel embedding of distributions,Algorithm,Machine learning,Injective metric space,Reproducing kernel Hilbert space,Mathematics | Journal |
Volume | Issue | ISSN |
5 | 1 | 1875-6891 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songhua Liu | 1 | 1 | 1.03 |
| Junying Zhang | 2 | 86 | 7.59 |
| Caiying Ding | 3 | 0 | 0.34 |