Preprocessed Spectral Clustering with Higher Connectivity for Robustness in Real-World Applications

Abstract

This paper introduces a novel model for spectral clustering to solve the problem of poor connectivity among points within the same cluster as this can negatively impact the performance of spectral clustering. The proposed method leverages both sparsity and connectivity properties within each cluster to find a consensus similarity matrix. More precisely, the proposed approach considers paths of varying lengths in the graph, computing a similarity matrix for each path, and generating a cluster for each path. By combining these clusters using multi-view spectral clustering, the method produces clusters of good quality and robustness when there are outliers and noise. The extracted multiple independent views from different paths in the graph are integrated into a consensus graph. The performance of the proposed method is evaluated on various benchmark datasets and compared to state-of-the-art techniques.