DocumentCode :
3383429
Title :
An affinity propagation algorithm base on self-tuning kernel geodesic distance
Author :
Jianpeng Zhang ; Fucai Chen ; Lixiong Liu ; Dongdong Niu
Author_Institution :
Nat. Digital Switching Syst. Eng. & Technol. R&D Center, Zhengzhou, China
fYear :
2013
fDate :
23-25 March 2013
Firstpage :
751
Lastpage :
756
Abstract :
For affinity propagation algorithm, traditional Euclidean distance measure cannot fully reflect the complex spatial distribution of the data sets. We propose a self-tuning kernel geodesic distance as the similarity measure which can reflect the inherent manifold structure information effectively. Meanwhile, according to the neighborhood density of the data sets, it identifies and eliminates the influence of boundary noise effectively, the results show that the improved algorithm has higher accuracy and better robustness for data with manifold distribution, multi-scale and noise overlap.
Keywords :
pattern clustering; Euclidean distance measure; affinity propagation algorithm; boundary noise; clustering analysis; complex spatial distribution; data sets; inherent manifold structure information; manifold distribution; neighborhood density; noise overlap; self-tuning kernel geodesic distance; similarity measure; Algorithm design and analysis; Clustering algorithms; Euclidean distance; Kernel; Level measurement; Manifolds; Noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2013 International Conference on
Conference_Location :
Yangzhou
Print_ISBN :
978-1-4673-5137-9
Type :
conf
DOI :
10.1109/ICIST.2013.6747653
Filename :
6747653
Link To Document :
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