Least absolute deviation cut

Authors:
Jian Yu;Liping Jing
Affiliations:
Department of Computer Science, Beijing Jiaotong University, Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong University, Beijing, P.R. China
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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Abstract

The paper discussed a new additive extension of minimum cut by simultaneously minimizing intra cluster similarity bias and inter cluster similarity, Least Absolute Deviation Cut (LAD cut). The LAD cut can be proved convergent in finite iterative steps, and its theoretical conditions that the LAD cut can work well is also presented. Furthermore, its computational complexity is also analyzed. Numerical experimental results show that LAD cut may be useful for image segmentation.