TY - GEN
T1 - Transitive distance clustering with K-means duality
AU - Yu, Zhiding
AU - Xu, Chunjing
AU - Meng, Deyu
AU - Hui, Zhuo
AU - Xiao, Fanyi
AU - Liu, Wenbo
AU - Liu, Jianzhuang
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/24
Y1 - 2014/9/24
N2 - We propose a very intuitive and simple approximation for the conventional spectral clustering methods. It effectively alleviates the computational burden of spectral clustering - reducing the time complexity from O(n3) to O(n2) - while capable of gaining better performance in our experiments. Specifically, by involving a more realistic and effective distance and the 'k-means duality' property, our algorithm can handle datasets with complex cluster shapes, multi-scale clusters and noise. We also show its superiority in a series of its real applications on tasks including digit clustering as well as image segmentation.
AB - We propose a very intuitive and simple approximation for the conventional spectral clustering methods. It effectively alleviates the computational burden of spectral clustering - reducing the time complexity from O(n3) to O(n2) - while capable of gaining better performance in our experiments. Specifically, by involving a more realistic and effective distance and the 'k-means duality' property, our algorithm can handle datasets with complex cluster shapes, multi-scale clusters and noise. We also show its superiority in a series of its real applications on tasks including digit clustering as well as image segmentation.
UR - https://www.scopus.com/pages/publications/84911380088
U2 - 10.1109/CVPR.2014.131
DO - 10.1109/CVPR.2014.131
M3 - 会议稿件
AN - SCOPUS:84911380088
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 987
EP - 994
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PB - IEEE Computer Society
T2 - 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Y2 - 23 June 2014 through 28 June 2014
ER -