Author:
Sadjadi Fatemeh,Torra Vicenç,Jamshidi Mina
Abstract
AbstractThis 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.
Funder
Knut och Alice Wallenbergs Stiftelse
Umea University
Publisher
Springer Science and Business Media LLC
Reference54 articles.
1. Han, J., Kamber, M., Pei, J.: Data Min. Mechanism Industrial Publishing, Concepts and Technology (2001)
2. Zass, R., Shashua, A.: Doubly stochastic normalization for spectral clustering. Advances in neural information processing systems. 19, (2019)
3. Huang, J., Zhang, T., Metaxas, D.: Learning with structured sparsity. In Proceedings of the 26th Annual International Conference on Machine Learning. 417-424 (2009)
4. Cai, Y., Huang, J.Z., Yin, J.: A new method to build the adaptive k-nearest neighbors similarity graph matrix for spectral clustering. Neurocomputing 493, 191–203 (2022)
5. Kang, Z., Lu, X., Yi, J., Xu, Z.: Self-weighted multiple kernel learning for graph-based clustering and semi-supervised classification. arXiv preprint arXiv:1806.07697 (2018)