Abstract
| Original language | English |
|---|---|
| Title of host publication | PRICAI 2010 |
| Subtitle of host publication | Trends in Artificial Intelligence. |
| Editors | Byoung-Tak Zhang, Mehmet A Orgun |
| Place of Publication | Germany |
| Publisher | Springer |
| Pages | 268-279 |
| Number of pages | 12 |
| Volume | 6230 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | Pacific Rim International Conference on Artificial Intelligence - Daegu, Korea, Korea, Republic of Duration: 30 Aug 2010 → 02 Sep 2010 |
Conference
| Conference | Pacific Rim International Conference on Artificial Intelligence |
|---|---|
| Country | Korea, Republic of |
| Period | 30/08/10 → 02/09/10 |
Cite this
}
Geometric Median-Shift over Riemannian Manifolds. / Wang, Yang; Huang, Xiaodi.
PRICAI 2010: Trends in Artificial Intelligence.. ed. / Byoung-Tak Zhang; Mehmet A Orgun. Vol. 6230 Germany : Springer, 2010. p. 268-279.Research output: Book chapter/Published conference paper › Conference paper
TY - GEN
T1 - Geometric Median-Shift over Riemannian Manifolds
AU - Wang, Yang
AU - Huang, Xiaodi
N1 - Imported on 03 May 2017 - DigiTool details were: publisher = Germany: Springer, 2010. editor/s (773b) = Byoung-Tak Zhang and Mehmet A Orgun; Event dates (773o) = 30-08-2010-02-09-2010; Parent title (773t) = Pacific Rim International Conference on Artificial Intelligence. ISSNs: 0302-9743;
PY - 2010
Y1 - 2010
N2 - Manifold clustering finds wide applications in many areas. In this paper, we propose a new kernel function that makes use of Riemannian geodesic distances among data points, and present a Geometric median shift algorithm over Riemannian Manifolds. Relying on the geometric median shift, together with geodesic distances, our approach is able to effectively cluster data points distributed over Riemannian manifolds. In addition to improving the clustering results, the complexity for calculating geometric median is reduced to O(n 2), compared to O(n 2logn 2) for Tukey median. Using both Riemannian Manifolds and Euclidean spaces, we compare the geometric median shift and mean shift algorithms for clustering synthetic and real data sets.
AB - Manifold clustering finds wide applications in many areas. In this paper, we propose a new kernel function that makes use of Riemannian geodesic distances among data points, and present a Geometric median shift algorithm over Riemannian Manifolds. Relying on the geometric median shift, together with geodesic distances, our approach is able to effectively cluster data points distributed over Riemannian manifolds. In addition to improving the clustering results, the complexity for calculating geometric median is reduced to O(n 2), compared to O(n 2logn 2) for Tukey median. Using both Riemannian Manifolds and Euclidean spaces, we compare the geometric median shift and mean shift algorithms for clustering synthetic and real data sets.
U2 - 10.1007/978-3-642-15246-7_26
DO - 10.1007/978-3-642-15246-7_26
M3 - Conference paper
VL - 6230
SP - 268
EP - 279
BT - PRICAI 2010
A2 - Zhang, Byoung-Tak
A2 - Orgun, Mehmet A
PB - Springer
CY - Germany
ER -