Large-Scale Spectral Clustering with Stochastic Nyström Approximation - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Large-Scale Spectral Clustering with Stochastic Nyström Approximation

(1) , (1) , (1)
1
Hongjie Jia
  • Function : Author
  • PersonId : 1051573
Liangjun Wang
  • Function : Author
Heping Song
  • Function : Author

Abstract

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.
Fichier principal
Vignette du fichier
498234_1_En_3_Chapter.pdf (167.61 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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

Licence

Attribution - CC BY 4.0

Identifiers

Cite

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⟩
19 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More