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.
Original language | English |
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Article number | 8712566 |
Pages (from-to) | 725-736 |
Number of pages | 12 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 31 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2020 |
Keywords
- Clustering
- graph cut
- large-scale data
- spectral clustering