Spectral Clustering Based on Relation-Invariable Persistent Formation

Abstract

In this paper, a spectral clustering method based on relation-invariable persistent formation (RIPF) is proposed, which can character the similarity graph succinctly. RIPF is an optimal formation, which can remain the formation by using least edges in the comminution graph. After generating the RIPF, the algorithm builds a network graph weighted according to distance between neighborhoods, and then applies spectral clustering. Three methods to construct similarity graphs in standard spectral clustering are ε -neighborhood graph, k- nearest neighbor graph, or fully connected graph. However, ε -neighborhood graph or k -nearest neighbor graph is sensitive to the parameters ε or k, and fully connected similarity graph of which is redundant. The similarity graph is constructed by generating RIPF, which can model the concise local neighborhood relationships by using the least edges in similarity graph. In this paper, a new algorithm is proposed to weaken the parameter dependence while maintaining the solution quality. Experimental results show that out algorithm performs as well as or even better than the state-of-the-art standard spectral methods.