基于深度学习的聚类研究综述

宗珀 宋, 黎伟 叶

摘要


随着网络时代的发展，数据量已具有规模大、结构复杂特性，传统聚类算法得不到应用满足，深度学习能够深层次的抓取数据特征，因此基于深度学习的聚类算法（CD）成为聚类的研究热点。论文结合 CD 研究现状进行归纳总结，首先，从神经网络方面介绍相关概念；其次，根据网络结构对已有的 CD 进行分类；最后，总结聚类算法应具备的扩展性、鲁棒性条件。

关键词


聚类；深度学习；神经网络；扩展性；鲁棒性

全文:

PDF

参考


Lecun Y, Bottou L. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE,1998,86(11):2278-2324.

Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation,2006,18(7):1527-1554.

Hinton G E. A practical guide to training restricted boltzmann machines[M]. Montavon G, ORR G B, Muller K R, Neural networks: tricks of the trade. Berlin: Springer,2012.

Hinton G E, Salakhutdinov R. Reducing the dimensionality of data with neural networks[J]. Science,2006,313(5786):504-507.

Kingma D P, Welling M. Auto-encoding variational bayes[J]. ArXiv Preprint ArXiv,2013(3):1312.

Goodfellow I J, Pouget A J, Mirza M, et al. Generative adversarial networks[C]//Advances in Neural Information Processing System. Cambridge: MIT press,2014.

Shaham U, Stanton K, Li H, et al. Spectral Net: spectral clustering using deep neural networks[C]//International Conference on Learning Representations. Piscataway: IEEE,2018.

Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communication of the ACM,2017,60(6):84-90.

Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and

HE K M, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE,2016.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. ArXiv Preprint ArXiv,2014(8):1409+1556.

Nina O, Moody J, Milligan C. A decoder-free approach for unsupervised clustering and manifold learning with random triplet mining[C]// Proceedings of the IEEE International Conference on Computer Vision Workshops. Piscataway: IEEE, 2019.

Huang J B, Gong S G, Zhu X T. Deep semantic clustering by partition confidence maximization[C]// Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition.

Chen X, Duan Y, Houthooft R, et al. Infogan: interpretable representation learning by information maximizing generative adversarial nets[C]// Advances in Neural Information Processing Systems. Cambridge: MIT press, 2016.

Mukherjee S, Asnani H, Lin E, et al. Cluster GAN: latent space clustering in generative adversarial networks[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2019.




DOI: https://doi.org/10.12346/etr.v3i10.4406

Refbacks

  • 当前没有refback。