<Previous Article In Issue
Volume 5, Issue 1, February 2012, Pages 197 - 208
INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES
Authors
Songhua Liu, Junying Zhang, Caiying Ding
Corresponding Author
Songhua Liu
Received 15 October 2010, Accepted 11 January 2012, Available Online 1 February 2012.
- DOI
- 10.1080/18756891.2012.670530How to use a DOI?
- Keywords
- Kernel methods, Similarity measure, Reproducing kernel Hilbert space, Non-metric distance
- 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.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
<Previous Article In Issue
Cite this article
TY - JOUR AU - Songhua Liu AU - Junying Zhang AU - Caiying Ding PY - 2012 DA - 2012/02/01 TI - INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES JO - International Journal of Computational Intelligence Systems SP - 197 EP - 208 VL - 5 IS - 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2012.670530 DO - 10.1080/18756891.2012.670530 ID - Liu2012 ER -