[1] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323. [2] GOTTLIEB L A, KONTOROVICH A, KRAUTHGAMER R. Adaptive metric dimensionality reduction[J]. Theoretical Computer Science, 2016, 620: 105-118. [3] 田守财, 孙喜利, 路永钢. 基于最近邻的随机非线性降维[J]. 计算机应用, 2016, 36(2): 377-381. (TIAN S C, SUN X L, LU Y G. Stochastic nonlinear dimensionality reduction based on nearest neighbors[J]. Journal of Computer Applications, 2016, 36(2): 377-381.) [4] MIKA S, SCHÖLKOPF B, SMOLA A, et al. Kernel PCA and de-noising in feature spaces[EB/OL]. [2017-01-10]. https://alex.smola.org/papers/1999/MikSchSmoMuletal99.pdf. [5] HASTIE T, TIBSHIRANI R. Discriminant analysis by Gaussian mixtures[J]. Journal of the Royal Statistical Society, 1996, 58(1): 155-176. [6] BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6): 1373-1396. [7] HINTON G, ROWEIS S. Stochastic neighbor embedding[J]. Advances in Neural Information Processing Systems, 2002, 41(4): 833-840. [8] VAN der MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(2605): 2579-2605. [9] LIPMAN Y, PUENTE J, DAUBECHIES I. Conformal Wasserstein distance: Ⅱ. computational aspects and extensions[J]. Mathematics of Computation, 2011, 82(281): 331-381. [10] CHAZAL F, COHEN-STEINER D, MÉRIGOT Q. Geometric inference for probability measures[J]. Foundations of Computational Mathematics, 2011, 11(6): 733-751. [11] SOLOMON J, DE GOES F, PEYRÉ G, et al. Convolutional Wasserstein distances: efficient optimal transportation on geometric domains[J]. ACM Transactions on Graphics 2015, 34(4): 513-526. [12] VAN ERVEN T, HARREMOS P. Rényi divergence and Kullback-Leibler divergence[J]. IEEE Transactions on Information Theory, 2012, 60(7): 3797-3820. [13] DE GOES F, COHEN-STEINER D, PIERRE A, et al. An optimal transport approach to robust reconstruction and simplification of 2D shapes [J]. Computer Graphics Forum, 2011, 30(5): 1593-1602. [14] AJEESH S S, INDU M S, SHERLY E. Performance analysis of classification algorithms applied to Caltech101 image database[C]//Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques. Piscataway, NJ: IEEE, 2014: 693-696. [15] BRAND M. Charting a manifold[EB/OL]. [2017-01-10]. https://papers.nips.cc/paper/2165-charting-a-manifold.pdf. [16] MOKBEL B, LUEKS W, GISBRECHT A, et al. Visualizing the quality of dimensionality reduction[J]. Neurocomputing, 2013, 112(1): 109-123. |