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Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power.
On information and sufficiency
Kullback, S. and Leibler, R. A. (1951) · 1951
Earlier work this paper cites.
Columbia object image library (COIL-20)
Nene, S. A., Nayar, S. K., Murase, H., et al. (1996) · 1996
Earlier work this paper cites.
The MNIST database of handwritten digits
LeCun, Y. (1998) · 1998
Earlier work this paper cites.
Data clustering: a review
Jain, A. K., Murty, M. N., and Flynn, P. J. (1999) · 1999
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., and Weiss, Y. (2002) · 2002
Earlier work this paper cites.
Cluster ensembles—a knowledge reuse framework for combining multiple partitions
Strehl, A. and Ghosh, J. (2002) · 2002
Earlier work this paper cites.
k-Means clustering via principal component analysis
Ding, C. and He, X. (2004) · 2004
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R. (2006) · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
van der Maaten, L. and Hinton, G. (2008) · 2008
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
Earlier work this paper cites.
Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
Vinh, N. X., Epps, J., and Bailey, J. (2010) · 2010
Earlier work this paper cites.
Locally consistent concept factorization for document clustering
Cai, D., He, X., and Han, J. (2011) · 2011
Cited alongside, same era.
Auto-encoder based data clustering
Song, C., Liu, F., Huang, Y., Wang, L., and Tan, T. (2013) · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Deep embedding network for clustering
Huang, P., Huang, Y., Wang, W., and Wang, L. (2014) · 2014
Cited alongside, same era.
A deep semi-nmf model for learning hidden representations
Trigeorgis, G., Bousmalis, K., Zafeiriou, S., and Schuller, B. (2014) · 2014
Cited alongside, same era.
Deep learning with nonparametric clustering
Chen, G. (2015) · 2015
Unsupervised learning using generative adversarial training and clustering
Premachandran, V. and Yuille, A. L. (2016) · 2016
Later among the works it cites.
Learning a task-specific deep architecture for clustering
Wang, Z., Chang, S., Zhou, J., Wang, M., and Huang, T. S. (2016) · 2016
Later among the works it cites.
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., and Farhadi, A. (2016) · 2016
Later among the works it cites.
Variational deep embedding: A generative approach to clustering
Zheng, Y., Tan, H., Tang, B., Zhou, H., et al. (2016) · 2016
Later among the works it cites.
Unsupervised multi-manifold clustering by learning deep representation
Chen, D., Lv, J., and Yi, Z. (2017) · 2017
Later among the works it cites.
Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization
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Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
Cited alongside, same era.
Infinite ensemble for image clustering
Liu, H., Shao, M., Li, S., and Fu, Y. (2016) · 2016
Cited alongside, same era.
Speaker identification and clustering using convolutional neural networks
Lukic, Y., Vogt, C., Dürr, O., and Stadelmann, T. (2016) · 2016
Cited alongside, same era.
Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Yang, B., Fu, X., Sidiropoulos, N. D., and Hong, M. (2016a)
Cited in the paper.
Joint unsupervised learning of deep representations and image clusters
Yang, J., Parikh, D., and Batra, D. (2016b)
Cited in the paper.
Dizaji, K. G., Herandi, A., Deng, C., Cai, W., and Huang, H. (2017) · 2017
Later among the works it cites.
Hsu, C.-C. and Lin, C.-W. (2017) · 2017
Later among the works it cites.
Learning discrete representations via information maximizing self augmented training
Hu, W., Miyato, T., Tokui, S., Matsumoto, E., and Sugiyama, M. (2017) · 2017
Later among the works it cites.
Discriminatively boosted image clustering with fully convolutional auto-encoders
Li, F., Qiao, H., Zhang, B., and Xi, X. (2017) · 2017
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Neural clustering: Concatenating layers for better projections
Saito, S. and Tan, R. T. (2017) · 2017
Later among the works it cites.