A review on spectral clustering and stochastic block models

Author:

Baek Mina,Kim Choongrak

Funder

MIST

Publisher

Springer Science and Business Media LLC

Subject

Statistics and Probability

Reference39 articles.

1. Amini, A. A., Chen, A., Bickel, P. J., & Levina, E. (2013). Pseudo-likelihood methods for community detection in large sparse networks. The Annals of Statistics, 41, 2097–2122.

2. Amini, A. A., & Levina, E. (2018). On semidefinite relaxations for the block model. The Annals of Statistics, 46, 149–179.

3. Belkin, M., & Niyogi, P. (2002). Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems, 15, 585–591.

4. Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373–1396.

5. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.

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