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Conference Papers Year : 2020

Large-Scale Spectral Clustering with Stochastic Nyström Approximation

Hongjie Jia
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Liangjun Wang
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Heping Song
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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.
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Dates and versions

hal-03456960 , version 1 (30-11-2021)





Hongjie Jia, Liangjun Wang, Heping Song. Large-Scale Spectral Clustering with Stochastic Nyström Approximation. 11th International Conference on Intelligent Information Processing (IIP), Jul 2020, Hangzhou, China. pp.26-34, ⟨10.1007/978-3-030-46931-3_3⟩. ⟨hal-03456960⟩
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