Abstract
Dimensionality reduction methods aimed at preserving the data topology have shown to be suitable for reaching high-quality embedded data. In particular, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comparative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them.
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References
Borg, I.: Modern multidimensional scaling: Theory and applications. Springer (2005)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Advances in Neural Information Processing Systems, pp. 833–840 (2002)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(2579-2605), 85 (2008)
Lee, J.A., Renard, E., Bernard, G., Dupont, P., Verleysen, M.: Type 1 and 2 mixtures of kullback-leibler divergences as cost functions in dimensionality reduction based on similarity preservation. Neurocomputing (2013)
Carreira-Perpinán, M.A.: The elastic embedding algorithm for dimensionality reduction. In: ICML, vol. 10, pp. 167–174 (2010)
Durbin, R., Szeliski, R., Yuille, A.: An analysis of the elastic net approach to the traveling salesman problem. Neural Computation 1(3), 348–358 (1989)
Vladymyrov, M., Carreira-Perpiñán, M.Á.: Partial-hessian strategies for fast learning of nonlinear embeddings. CoRR, abs/1206.4646 (2012)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (coil-20). Dept. Comput. Sci., Columbia Univ., New York, 62 (1996), http://www.cs.columbia.edu/CAVE/coil-20.html
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Venna, J., Peltonen, J., Nybo, K., Aidos, H., Kaski, S.: Information retrieval perspective to nonlinear dimensionality reduction for data visualization. The Journal of Machine Learning Research 11, 451–490 (2010)
Nocedal, J., Wright, S.: Numerical optimization. Series in operations research and financial engineering. Springer, New York (2006)
Yu, S.X., Shi, J.: Multiclass spectral clustering. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 313–319. IEEE (2003)
Singer, A., Wu, H.-T.: Vector diffusion maps and the connection Laplacian. Communications on Pure and Applied Mathematics 65(8), 1067–1144 (2012)
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Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M. (2014). Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_6
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DOI: https://doi.org/10.1007/978-3-319-07695-9_6
Publisher Name: Springer, Cham
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