Large Scale Spectral Clustering Via Landmark-Based Sparse Representation

By Deng Cai, Xinlei Chen. IEEE Transactions on Cybernetics, 2015

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Unsupervised Large Graph Embedding (2017, methodology): “To alleviate the problem, inspired by recent progresses on scalable semi-supervised learning (Liu, He, and Chang 2010), large scale spectral clustering (Cai and Chen 2015)(Li et al.”
Ultra-Scalable Spectral Clustering and Ensemble Clustering (2019, methodology): “However, these sub-matrix based spectral clustering methods [3], [4] are typically restricted by an OðNpÞ complexity bottleneck,which has been a critical hurdle”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “…c l u s t e r i ng m e t hod s (Zahn 1971; Guan and Du 1998), CLICK (Sharan and Shamir 2000), Spectral clustering (von Luxburg 2007), improvements of Spectral clustering (Chen et al. 2011; Cai and Chen 2015; He et al. 2019; Huang et al. 2020), and Kemighan–Lin (KL) method (Kernighan and Lin 1970).”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, methodology): “Spectral clustering [Ng et al. , 2001; Zelnik-Manor and Per-ona, 2004; Cai and Chen, 2015; Nie et al. , 2011 ] aims at exploring the local nonlinear manifold (spectral graph) 1 structure [ Hou et al. , 2015; Tao et al. , 2016 ] , attracting great attention within recent years.”