Large-Scale Spectral Clustering with Stochastic Nyström Approximation
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In spectral clustering, Nyström approximation is a powerful technique to reduce the time and space cost of matrix decomposition. However, in order to ensure the accurate approximation, a sufficient number of samples are needed. In very large datasets, the internal singular value decomposition (SVD) of Nyström will also spend a large amount of calculation and almost impossible. To solve this problem, this paper proposes a large-scale spectral clustering algorithm with stochastic Nyström approximation. This algorithm uses the stochastic low rank matrix approximation technique to decompose the sampled sub-matrix within the Nyström procedure, losing a slight of accuracy in exchange for a significant improvement of the algorithm efficiency. The performance of the proposed algorithm is tested on benchmark data sets and the clustering results demonstrate its effectiveness.
KeywordsSpectral clustering Nyström approximation Stochastic SVD Large dataset
This work was supported by the National Natural Science Foundations of China (grant numbers 61906077, 61601202), the Natural Science Foundation of Jiangsu Province (grant numbers BK20190838, BK20170558), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (grant number 18KJB520009, 16KJB520008).
- 1.Kang, Z., et al.: Multi-graph fusion for multi-view spectral clustering. Knowl.-Based Syst. 189 (2020). https://doi.org/10.1016/j.knosys.2019.105102
- 5.Li, M., Bi, W., Kwok, J.T., Lu, B.L.: Large-scale Nyström kernel matrix approximation using randomized SVD. IEEE Trans. Neural Netw. Learn. Syst. 26(1), 152–164 (2014)Google Scholar
- 10.Chitta, R., Jin, R., Havens, T.C., Jain, A.K.: Approximate kernel k-means: solution to large scale kernel clustering. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 895–903. ACM, San Diego (2011)Google Scholar