Name
Abstract | ||
|---|---|---|
Spectral clustering belongs to unsupervised learning. As for most unsupervised methods, how to encode semi-supervised constrains into spectral clustering remains a developing issue. In the algorithm of spectral clustering, the eigen-decomposition suffers from severe computational complexity. In this paper, we propose constrained spectral clustering using Nyström Method. By modifying the graph adjacency matrix, we incorporate the semi-supervised constrains into the spectral clustering. Meanwhile, it’s the aim to approximately produce a linear time algorithm through combining the Nyström method with spectral clustering algorithm. In the experiment, we validate the proposed algorithm on real-world and synthetic dataset. Compared with other cluster methods, the proposed algorithm has better performance in clustering accuracy and computational complexity. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.procs.2018.03.036 | Procedia Computer Science |
Keywords | DocType | Volume |
Spectral Clustering,Semi-supervised Constrains,the Nyström Method | Conference | 129 |
ISSN | Citations | PageRank |
1877-0509 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangchi Li | 1 | 0 | 1.01 |
| Shenling Wang | 2 | 0 | 1.01 |
| Shuaijing Xu | 3 | 0 | 1.69 |
| Yuqi Yang | 4 | 0 | 1.35 |