Multi-manifold Discriminant Isomap for visualization and classification
暂无分享,去创建一个
Bo Yang | Ming Xiang | Yupei Zhang | Yupei Zhang | M. Xiang | Bo Yang
[1] Gang Wang,et al. Discriminative multi-manifold analysis for face recognition from a single training sample per person , 2011, 2011 International Conference on Computer Vision.
[2] Feiping Nie,et al. Trace Ratio Problem Revisited , 2009, IEEE Transactions on Neural Networks.
[3] M. Sniedovich. Dynamic programming : foundations and principles , 2011 .
[4] Jun Li,et al. Nonparametric discriminant multi-manifold learning for dimensionality reduction , 2015, Neurocomputing.
[5] Lei Zhang,et al. A multi-manifold discriminant analysis method for image feature extraction , 2011, Pattern Recognit..
[6] W. Wong,et al. Supervised optimal locality preserving projection , 2012, Pattern Recognit..
[7] Yu Zhang,et al. Nonlinear Expectation Maximization Estimator for TDOA Localization , 2014, IEEE Wireless Communications Letters.
[8] Hongping Cai,et al. Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors , 2011, IEEE Trans. Pattern Anal. Mach. Intell..
[9] Shuicheng Yan,et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007 .
[10] J. Leeuw,et al. Simple and Canonical Correspondence Analysis Using the R Package anacor , 2007 .
[11] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[12] Patrick Mair,et al. Multidimensional Scaling Using Majorization: SMACOF in R , 2008 .
[13] Zi Huang,et al. Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis , 2012, Pattern Recognition.
[14] Bo Yang,et al. Linear dimensionality reduction based on Hybrid structure preserving projections , 2016, Neurocomputing.
[15] Zi Huang,et al. Self-taught dimensionality reduction on the high-dimensional small-sized data , 2013, Pattern Recognit..
[16] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[17] Yiu-ming Cheung,et al. Semi-Supervised Maximum Margin Clustering with Pairwise Constraints , 2012, IEEE Transactions on Knowledge and Data Engineering.
[18] Tommy W. S. Chow,et al. M-Isomap: Orthogonal Constrained Marginal Isomap for Nonlinear Dimensionality Reduction , 2013, IEEE Transactions on Cybernetics.
[19] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[20] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[21] P. Groenen,et al. Modern multidimensional scaling , 1996 .
[22] Hwann-Tzong Chen,et al. Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[23] Matti Pietikäinen,et al. Supervised Locally Linear Embedding , 2003, ICANN.
[24] Fadi Dornaika,et al. Exponential Local Discriminant Embedding and Its Application to Face Recognition , 2013, IEEE Transactions on Cybernetics.
[25] Jim Jing-Yan Wang,et al. Discriminative sparse coding on multi-manifolds , 2013, Knowl. Based Syst..
[26] W. Scott Spangler,et al. Class visualization of high-dimensional data with applications , 2002, Comput. Stat. Data Anal..
[27] Dong Xu,et al. Trace Ratio vs. Ratio Trace for Dimensionality Reduction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[28] Dimitrios Gunopulos,et al. Non-linear dimensionality reduction techniques for classification and visualization , 2002, KDD.
[29] Patrick J. F. Groenen,et al. Modern Multidimensional Scaling: Theory and Applications , 2003 .
[30] Deng Cai,et al. Laplacian Score for Feature Selection , 2005, NIPS.
[31] Peng Li,et al. Distance Metric Learning with Eigenvalue Optimization , 2012, J. Mach. Learn. Res..
[32] Tommy W. S. Chow,et al. Trace Ratio Optimization-Based Semi-Supervised Nonlinear Dimensionality Reduction for Marginal Manifold Visualization , 2013, IEEE Transactions on Knowledge and Data Engineering.
[33] Alexander M. Bronstein,et al. Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding , 2010, International Journal of Computer Vision.
[34] Xiaofei He,et al. Locality Preserving Projections , 2003, NIPS.
[35] Hongbin Zha,et al. Riemannian Manifold Learning , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Xiangyang Xue,et al. Discriminant neighborhood embedding for classification , 2006, Pattern Recognit..
[37] Zhi-Hua Zhou,et al. Supervised nonlinear dimensionality reduction for visualization and classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[38] T. Cacoullos,et al. Discriminant analysis and applications , 1974 .
[39] Ameet Talwalkar,et al. Sampling Methods for the Nyström Method , 2012, J. Mach. Learn. Res..
[40] Xuelong Li,et al. Discriminant Locally Linear Embedding With High-Order Tensor Data , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[41] S. Dragomir. A Survey on Cauchy-Buniakowsky-Schwartz Type Discrete Inequalities , 2003 .
[42] Bo Yang,et al. Learning discriminant isomap for dimensionality reduction , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[43] Jiwen Lu,et al. Multi-feature multi-manifold learning for single-sample face recognition , 2014, Neurocomputing.
[44] Stefan Hougardy,et al. The Floyd-Warshall algorithm on graphs with negative cycles , 2010, Inf. Process. Lett..
[45] Jiawei Han,et al. Document clustering using locality preserving indexing , 2005, IEEE Transactions on Knowledge and Data Engineering.
[46] James F. Peters,et al. Multi-manifold LLE learning in pattern recognition , 2015, Pattern Recognit..
[47] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[48] Nicolas Le Roux,et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.
[49] Nanning Zheng,et al. Statistical Learning and Pattern Analysis for Image and Video Processing , 2009, Advances in Pattern Recognition.
[50] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.