Clustering as physically inspired energy minimization
作者：
Highlights：
• We more completely map the energy model of statistical physics onto clustering problem, and our method can be totally unsupervised.
• We make a perfect analogy with the energy model used in vision and borrow the methods from vision field to clustering under this mapping.
• We propose a data point local density estimation method which can account for the datasets with arbitrary shapes and topologies.
• We point out that the energy model of spectral clustering methods (such as Normalized-cut [22]) is incomplete compared with our energy model.
摘要
•We more completely map the energy model of statistical physics onto clustering problem, and our method can be totally unsupervised.•We make a perfect analogy with the energy model used in vision and borrow the methods from vision field to clustering under this mapping.•We propose a data point local density estimation method which can account for the datasets with arbitrary shapes and topologies.•We point out that the energy model of spectral clustering methods (such as Normalized-cut [22]) is incomplete compared with our energy model.
论文关键词：Unsupervised/hierarchical clustering,Energy minimization,Statistical physics,Integer programming,Unary/data energy,Ising model,Local density,Connected component analysis,Image segmentation,Normalized-cut
论文评审过程：Received 11 April 2017, Revised 1 August 2018, Accepted 10 September 2018, Available online 20 September 2018, Version of Record 1 October 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.09.008
