Nonlinear Dimensionality Reduction by John A. Lee; Michel VerleysenDimensionality reduction is used in almost all areas of science for visualizing, preprocessing, or gaining a better understanding of high dimensional data. Linear methods have been used since the introduction of principal component ...
Dimensionality reduction (DR) is a widely used technique to address the curse of dimensionality when high-dimensional remotely sensed data, such as multi-temporal or hyperspectral imagery, are analyzed. Nonlinear DR algorithms, also referred to as manifold learning algorithms, have been successfully app...
K Kim,J Lee - 《Pattern Recognition》 被引量: 47发表: 2014年 Semisupervised kernel marginal Fisher analysis for face recognition. Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel ... Z ...
Nonlinear dimensionality reduction in climate data. Nonlinear Processes in Geophysics 11, 393–398 (2004). 26. Ross, I., Valdes, P. & Wiggins, S. ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction. Nonlinear Processes in Geophysics 15, 339–363, ...
Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two nonlinear projection met... J Lee,A Lendasse,M Verleysen 被引量: 0发表: 2002年 Comparison of visualization methods for an atlas of gene expression data sets This ...
Do Hyung Lee,Dookie Kim,Kihak Lee, Nonlinear Dynamics 2009 原文传递 原文传递并翻译 示例 加入购物车 收藏 分享 50 Synchronization of four coupled van der Pol oscillators 机译:四个耦合范德波尔振荡器的同步 Miguel A. Barrón,Mihir Sen, Nonlinear Dynamics 2009 原文传递 原文传递并翻译 示例 ...
In the context of MOR, a VAE can be considered as a Bayesian implementation of a deterministic autoencoder; a popular deterministic deep learning technique, which has been often exploited for dimensionality reduction. Lee and Carlberg56 make use of a convolutional autoencoder, in conjunction with ...
44. Lee J, Verleysen M. Nonlinear Dimensionality Reduction, Information Science and Statistics. Springer: New York, NY, 2007. 45. Sammon JW. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 1969; 18:401–409. 46. Hérault J, Oliva A, Guérin-Dugué A. Scene...
Various techniques for data reduction have been proposed in the literature (Maaten et al., 2009). Among these techniques, principal component analysis (PCA) (Jackson, 1991) is one of the most used. Thus, Liao and Lee (2009) utilized the PCA to extract features by using wavelet packet ...
M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509 (2014). Article CAS PubMed PubMed Central Google Scholar Pandarinath, C. et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15, 805–815 (2018). ...