Mixing autoencoder with classifier: conceptual data visualization

J. B. Kruskal, “Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis,” Psychometrika , vol. 29, no. 1, pp. 1–27, Mar 1964. [Online]. Available: https://doi.org/10.1007/BF02289565

1964

Earlier work this paper cites.

J. W. Sammon, “A nonlinear mapping for data structure analysis,” IEEE Transactions on Computers , vol. C-18, no. 5, pp. 401–409, May 1969

1969

Earlier work this paper cites.

T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biological Cybernetics , vol. 43, pp. 59–69, 1982

1982

Earlier work this paper cites.

J. B. Tenenbaum, “Mapping a manifold of perceptual observations,” in Advances in Neural Information Processing Systems 10 , M. I. Jordan, M. J. Kearns, and S. A. Solla, Eds. MIT Press, 1998, pp. 682–688. [Online]. Available: http://papers.nips.cc/paper/1332-mapping-a-manifold-of-perceptual-observations.pdf

1998

Earlier work this paper cites.

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science , vol. 290, no. 5500, pp. 2323–2326, 2000. [Online]. Available: https://science.sciencemag.org/content/290/5500/2323

2000

Earlier work this paper cites.

J. B. Tenenbaum, V. d. Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science , vol. 290, no. 5500, pp. 2319–2323, 2000. [Online]. Available: https://science.sciencemag.org/content/290/5500/2319

2000

Earlier work this paper cites.

G. E. Hinton and S. T. Roweis, “Stochastic neighbor embedding,” in Advances in Neural Information Processing Systems 15 , S. Becker, S. Thrun, and K. Obermayer, Eds. MIT Press, 2003, pp. 857–864. [Online]. Available: http://papers.nips.cc/paper/2276-stochastic-neighbor-embedding.pdf

2003

Cited alongside, same era.

J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, “Neighbourhood components analysis,” in Proceedings of the 17th International Conference on Neural Information Processing Systems , ser. NIPS’04. Cambridge, MA, USA: MIT Press, 2004, pp. 513–520. [Online]. Available: http://dl.acm.org/citation.cfm?id=2976040.2976105

2004

Cited alongside, same era.

G. Hinton and R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science , vol. 313, no. 5786, pp. 504—507, 2006

2006

Cited alongside, same era.

L. van der Maaten, “Visualizing high-dimensional data using t-sne,” Journal of Machine Learning Research , vol. 9, pp. 2579–2605, 2008

2008

P. Hartono, P. Hollensen, and T. Trappenberg, “Learning-regulated context relevant topographical map,” IEEE Trans. on Neural Networks and Learning Systems , vol. 26, no. 10, pp. 2323–2335, 2015

2015

Later among the works it cites.

P. Hartono, “Classification and dimensional reduction using restricted radial basis function networks,” Neural Computing and Applications , 2016

2016

Later among the works it cites.

P. Hartono and Y. Take, “Pairwise elastic self-organizing maps,” in 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) , June 2017, pp. 1–7

2017

Later among the works it cites.

L. McInnes, J. Healy, and J. Melville, “Umap: Uniform manifold approximation and projection for dimension reduction,” 2018

2018

Later among the works it cites.