Name
Abstract | ||
|---|---|---|
Although many spectral clustering algorithms have been proposed during the past decades, they are not scalable to large-scale data due to their high computational complexities. In this paper, we propose a novel spectral clustering method for large-scale data, namely, large-scale balanced min cut (LABIN). A new model is proposed to extend the self-balanced min-cut (SBMC) model with the anchor-based strategy and a fast spectral rotation with linear time complexity is proposed to solve the new model. Extensive experimental results show the superior performance of our proposed method in comparison with the state-of-the-art methods including SBMC. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TNNLS.2019.2909425 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
Computational modeling,Clustering algorithms,Computational complexity,Data models,Clustering methods,Laplace equations,Learning systems | Pattern recognition,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 3 | 2162-237X |
Citations | PageRank | References |
2 | 0.35 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojun Chen | 1 | 1298 | 107.51 |
| Renjie Chen | 2 | 2 | 0.69 |
| Wu Qingyao | 3 | 259 | 33.46 |
| Yixiang Fang | 4 | 227 | 23.06 |
| Feiping Nie | 5 | 7061 | 309.42 |
| Joshua Zhexue Huang | 6 | 1365 | 82.64 |