非线性降维 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "非线性降维" in Chinese language version.

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acm.org

dl.acm.org

Zhang, Z.; Wang, J. MLLE: Modified Locally Linear Embedding Using Multiple Weights. NIPS'06: Proceedings of the 19th International Conference on Neural Information Processing Systems. 2006: 1593–1600.

arxiv.org

Lawrence, Neil D. A unifying probabilistic perspective for spectral dimensionality reduction: insights and new models. Journal of Machine Learning Research. 2012, 13 (May): 1609–38. Bibcode:2010arXiv1010.4830L. arXiv:1010.4830 .
Haller, George; Ponsioen, Sten. Nonlinear normal modes and spectral submanifolds: Existence, uniqueness and use in model reduction. Nonlinear Dynamics. 2016, 86 (3): 1493–1534. S2CID 44074026. arXiv:1602.00560 . doi:10.1007/s11071-016-2974-z.
Gorban, A. N.; Zinovyev, A. Principal manifolds and graphs in practice: from molecular biology to dynamical systems. International Journal of Neural Systems. 2010, 20 (3): 219–232. PMID 20556849. S2CID 2170982. arXiv:1001.1122 . doi:10.1142/S0129065710002383.
Sidhu, Gagan. Locally Linear Embedding and fMRI feature selection in psychiatric classification. IEEE Journal of Translational Engineering in Health and Medicine. 2019, 7: 1–11. PMC 6726465 . PMID 31497410. S2CID 201832756. arXiv:1908.06319 . doi:10.1109/JTEHM.2019.2936348.
Taylor, D.; Klimm, F.; Harrington, H. A.; Kramár, M.; Mischaikow, K.; Porter, M. A.; Mucha, P. J. Topological data analysis of contagion maps for examining spreading processes on networks. Nature Communications. 2015, 6: 7723. Bibcode:2015NatCo...6.7723T. PMC 4566922 . PMID 26194875. arXiv:1408.1168 . doi:10.1038/ncomms8723.
McInnes, Leland; Healy, John; Melville, James. Uniform manifold approximation and projection for dimension reduction. 2018-12-07. arXiv:1802.03426 .

byu.edu

axon.cs.byu.edu

Gashler, M.; Martinez, T. Temporal Nonlinear Dimensionality Reduction (PDF). Proceedings of the International Joint Conference on Neural Networks IJCNN'11: 1959–66. 2011.
Gashler, M. and Ventura, D. and Martinez, T., Iterative Non-linear Dimensionality Reduction with Manifold Sculpting, In Platt, J.C. and Koller, D. and Singer, Y. and Roweis, S., editor, Advances in Neural Information Processing Systems 20, pp. 513–520, MIT Press, Cambridge, MA, 2008

caltech.edu

its.caltech.edu

Belkin, Mikhail; Niyogi, Partha. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering (PDF). Advances in Neural Information Processing Systems (MIT Press). 2001, 14: 586–691. ISBN 0-262-27173-7. OCLC 52710683.

curie.fr

bioinfo-out.curie.fr

A. Zinovyev, ViDaExpert - Multidimensional Data Visualization Tool Institut Curie, Paris.
ELastic MAPs. [2016-05-30]. （原始内容存档于2011-07-20）.

doi.org

Haller, George; Ponsioen, Sten. Nonlinear normal modes and spectral submanifolds: Existence, uniqueness and use in model reduction. Nonlinear Dynamics. 2016, 86 (3): 1493–1534. S2CID 44074026. arXiv:1602.00560 . doi:10.1007/s11071-016-2974-z.
Schölkopf, B.; Smola, A.; Müller, K.-R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation (MIT Press). 1998, 10 (5): 1299–1319. S2CID 6674407. doi:10.1162/089976698300017467.
Ham, Jihun; Lee, Daniel D.; Mika, Sebastian; Schölkopf, Bernhard. A kernel view of the dimensionality reduction of manifolds. Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004. doi:10.1145/1015330.1015417.
Gorban, A. N.; Zinovyev, A. Principal manifolds and graphs in practice: from molecular biology to dynamical systems. International Journal of Neural Systems. 2010, 20 (3): 219–232. PMID 20556849. S2CID 2170982. arXiv:1001.1122 . doi:10.1142/S0129065710002383.
Hastie, T.; Stuetzle, W. Principal Curves (PDF). Journal of the American Statistical Association. June 1989, 84 (406): 502–6. doi:10.1080/01621459.1989.10478797.
Tenenbaum, J B.; de Silva, V.; Langford, J.C. A Global Geometric Framework for Nonlinear Dimensionality Reduction (PDF). Science. 2000, 290 (5500): 2319–23. Bibcode:2000Sci...290.2319T. PMID 11125149. S2CID 221338160. doi:10.1126/science.290.5500.2319.
Roweis, S. T.; Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science. 2000, 290 (5500): 2323–6. Bibcode:2000Sci...290.2323R. PMID 11125150. S2CID 5987139. doi:10.1126/science.290.5500.2323.
Donoho, D.; Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 2003, 100 (10): 5591–6. Bibcode:2003PNAS..100.5591D. PMC 156245 . PMID 16576753. doi:10.1073/pnas.1031596100 .
Sidhu, Gagan. Locally Linear Embedding and fMRI feature selection in psychiatric classification. IEEE Journal of Translational Engineering in Health and Medicine. 2019, 7: 1–11. PMC 6726465 . PMID 31497410. S2CID 201832756. arXiv:1908.06319 . doi:10.1109/JTEHM.2019.2936348.
Zhang, Zhenyue; Hongyuan Zha. Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment. SIAM Journal on Scientific Computing. 2005, 26 (1): 313–338. CiteSeerX 10.1.1.211.9957 . doi:10.1137/s1064827502419154.
Ding, M.; Fan, G. Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling. IEEE Transactions on Cybernetics. 2015, 45 (11): 2413–24. PMID 25532201. S2CID 15591304. doi:10.1109/TCYB.2014.2373393.
Li, James X. Visualizing high-dimensional data with relational perspective map (PDF). Information Visualization. 2004, 3: 49–59. S2CID 7566939. doi:10.1057/palgrave.ivs.9500051.
Taylor, D.; Klimm, F.; Harrington, H. A.; Kramár, M.; Mischaikow, K.; Porter, M. A.; Mucha, P. J. Topological data analysis of contagion maps for examining spreading processes on networks. Nature Communications. 2015, 6: 7723. Bibcode:2015NatCo...6.7723T. PMC 4566922 . PMID 26194875. arXiv:1408.1168 . doi:10.1038/ncomms8723.
Demartines, P.; Hérault, J. Curvilinear Component Analysis: A Self-Organizing Neural Network for Nonlinear Mapping of Data Sets (PDF). IEEE Transactions on Neural Networks. 1997, 8 (1): 148–154. PMID 18255618. doi:10.1109/72.554199.
Coifman, Ronald R.; Lafon, Stephane. Diffusion Maps (PDF). Applied and Computational Harmonic Analysis. July 2006, 21 (1): 5–30. S2CID 17160669. doi:10.1016/j.acha.2006.04.006.
Venna, J.; Kaski, S. Local multidimensional scaling. Neural Networks. 2006, 19 (6–7): 889–899. PMID 16787737. doi:10.1016/j.neunet.2006.05.014.
Scholz, M.; Kaplan, F.; Guy, C. L.; Kopka, J.; Selbig, J. Non-linear PCA: a missing data approach. Bioinformatics (Oxford University Press). 2005, 21 (20): 3887–95. PMID 16109748. doi:10.1093/bioinformatics/bti634 . hdl:11858/00-001M-0000-0014-2B1F-2 .
Rosman, G.; Bronstein, M.M.; Bronstein, A.M.; Kimmel, R. Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding (PDF). International Journal of Computer Vision. 2010, 89 (1): 56–68. S2CID 1365750. doi:10.1007/s11263-010-0322-1.

dtic.mil

apps.dtic.mil

Hastie, T. Principal Curves and Surfaces (PDF) (学位论文). Stanford Linear Accelerator Center, Stanford University. November 1984. （原始内容存档 (PDF)于2019-08-02）.

handle.net

hdl.handle.net

Scholz, M.; Kaplan, F.; Guy, C. L.; Kopka, J.; Selbig, J. Non-linear PCA: a missing data approach. Bioinformatics (Oxford University Press). 2005, 21 (20): 3887–95. PMID 16109748. doi:10.1093/bioinformatics/bti634 . hdl:11858/00-001M-0000-0014-2B1F-2 .

harvard.edu

ui.adsabs.harvard.edu

Lawrence, Neil D. A unifying probabilistic perspective for spectral dimensionality reduction: insights and new models. Journal of Machine Learning Research. 2012, 13 (May): 1609–38. Bibcode:2010arXiv1010.4830L. arXiv:1010.4830 .
Tenenbaum, J B.; de Silva, V.; Langford, J.C. A Global Geometric Framework for Nonlinear Dimensionality Reduction (PDF). Science. 2000, 290 (5500): 2319–23. Bibcode:2000Sci...290.2319T. PMID 11125149. S2CID 221338160. doi:10.1126/science.290.5500.2319.
Roweis, S. T.; Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science. 2000, 290 (5500): 2323–6. Bibcode:2000Sci...290.2323R. PMID 11125150. S2CID 5987139. doi:10.1126/science.290.5500.2323.
Donoho, D.; Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 2003, 100 (10): 5591–6. Bibcode:2003PNAS..100.5591D. PMC 156245 . PMID 16576753. doi:10.1073/pnas.1031596100 .
Taylor, D.; Klimm, F.; Harrington, H. A.; Kramár, M.; Mischaikow, K.; Porter, M. A.; Mucha, P. J. Topological data analysis of contagion maps for examining spreading processes on networks. Nature Communications. 2015, 6: 7723. Bibcode:2015NatCo...6.7723T. PMC 4566922 . PMID 26194875. arXiv:1408.1168 . doi:10.1038/ncomms8723.

ieee.org

ieeexplore.ieee.org

Ding, M.; Fan, G. Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling. IEEE Transactions on Cybernetics. 2015, 45 (11): 2413–24. PMID 25532201. S2CID 15591304. doi:10.1109/TCYB.2014.2373393.

jmlr.org

Lawrence, Neil D. A unifying probabilistic perspective for spectral dimensionality reduction: insights and new models. Journal of Machine Learning Research. 2012, 13 (May): 1609–38. Bibcode:2010arXiv1010.4830L. arXiv:1010.4830 .
van der Maaten, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data Using t-SNE (PDF). Journal of Machine Learning Research. 2008, 9: 2579–2605.

le.ac.uk

math.le.ac.uk

The illustration is prepared using free software: Mirkes, E.M. Principal Component Analysis and Self-Organizing Maps: applet. University of Leicester. 2011.

mit.edu

jmlr.csail.mit.edu

Lawrence, N. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research. 2005, 6: 1783–1816.

narod.ru

pca.narod.ru

Yin, Hujun. 3. Learning Nonlinear Principal Manifolds by Self-Organising Maps. Gorban, A.N.; Kégl, B.; Wunsch, D.C.; Zinovyev, A. (编). Principal Manifolds for Data Visualization and Dimension Reduction. Lecture Notes in Computer Science and Engineering 58. Springer. 2007: 68–95. ISBN 978-3-540-73749-0.

nih.gov

ncbi.nlm.nih.gov

Gorban, A. N.; Zinovyev, A. Principal manifolds and graphs in practice: from molecular biology to dynamical systems. International Journal of Neural Systems. 2010, 20 (3): 219–232. PMID 20556849. S2CID 2170982. arXiv:1001.1122 . doi:10.1142/S0129065710002383.
Tenenbaum, J B.; de Silva, V.; Langford, J.C. A Global Geometric Framework for Nonlinear Dimensionality Reduction (PDF). Science. 2000, 290 (5500): 2319–23. Bibcode:2000Sci...290.2319T. PMID 11125149. S2CID 221338160. doi:10.1126/science.290.5500.2319.
Roweis, S. T.; Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science. 2000, 290 (5500): 2323–6. Bibcode:2000Sci...290.2323R. PMID 11125150. S2CID 5987139. doi:10.1126/science.290.5500.2323.
Donoho, D.; Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 2003, 100 (10): 5591–6. Bibcode:2003PNAS..100.5591D. PMC 156245 . PMID 16576753. doi:10.1073/pnas.1031596100 .
Sidhu, Gagan. Locally Linear Embedding and fMRI feature selection in psychiatric classification. IEEE Journal of Translational Engineering in Health and Medicine. 2019, 7: 1–11. PMC 6726465 . PMID 31497410. S2CID 201832756. arXiv:1908.06319 . doi:10.1109/JTEHM.2019.2936348.
Ding, M.; Fan, G. Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling. IEEE Transactions on Cybernetics. 2015, 45 (11): 2413–24. PMID 25532201. S2CID 15591304. doi:10.1109/TCYB.2014.2373393.
Taylor, D.; Klimm, F.; Harrington, H. A.; Kramár, M.; Mischaikow, K.; Porter, M. A.; Mucha, P. J. Topological data analysis of contagion maps for examining spreading processes on networks. Nature Communications. 2015, 6: 7723. Bibcode:2015NatCo...6.7723T. PMC 4566922 . PMID 26194875. arXiv:1408.1168 . doi:10.1038/ncomms8723.
Demartines, P.; Hérault, J. Curvilinear Component Analysis: A Self-Organizing Neural Network for Nonlinear Mapping of Data Sets (PDF). IEEE Transactions on Neural Networks. 1997, 8 (1): 148–154. PMID 18255618. doi:10.1109/72.554199.
Venna, J.; Kaski, S. Local multidimensional scaling. Neural Networks. 2006, 19 (6–7): 889–899. PMID 16787737. doi:10.1016/j.neunet.2006.05.014.
Scholz, M.; Kaplan, F.; Guy, C. L.; Kopka, J.; Selbig, J. Non-linear PCA: a missing data approach. Bioinformatics (Oxford University Press). 2005, 21 (20): 3887–95. PMID 16109748. doi:10.1093/bioinformatics/bti634 . hdl:11858/00-001M-0000-0014-2B1F-2 .

nips.cc

papers.nips.cc

Bengio, Yoshua; Paiement, Jean-Francois; Vincent, Pascal; Delalleau, Olivier; Le Roux, Nicolas; Ouimet, Marie. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering (PDF). Advances in Neural Information Processing Systems 16. MIT Press. 2004. ISBN 0-262-20152-6.
Walder, Christian; Schölkopf, Bernhard. Diffeomorphic Dimensionality Reduction (PDF). Advances in Neural Information Processing Systems 22. MIT Press. 2009: 1713–20.

ohio-state.edu

web.cse.ohio-state.edu

Belkin, Mikhail. Problems of Learning on Manifolds (学位论文). Department of Mathematics, The University of Chicago. August 2003. Matlab code for Laplacian Eigenmaps can be found in algorithms at Ohio-state.edu

cse.ohio-state.edu

Belkin, Mikhail. Problems of Learning on Manifolds (学位论文). Department of Mathematics, The University of Chicago. August 2003. Matlab code for Laplacian Eigenmaps can be found in algorithms at Ohio-state.edu

ox.ac.uk

robots.ox.ac.uk

Tenenbaum, J B.; de Silva, V.; Langford, J.C. A Global Geometric Framework for Nonlinear Dimensionality Reduction (PDF). Science. 2000, 290 (5500): 2319–23. Bibcode:2000Sci...290.2319T. PMID 11125149. S2CID 221338160. doi:10.1126/science.290.5500.2319.

solo.bodleian.ox.ac.uk

Bah, B. Diffusion Maps: Applications and Analysis (学位论文). University of Oxford. 2008.

palgrave-journals.com

Li, James X. Visualizing high-dimensional data with relational perspective map (PDF). Information Visualization. 2004, 3: 49–59. S2CID 7566939. doi:10.1057/palgrave.ivs.9500051.

psu.edu

citeseerx.ist.psu.edu

Zhang, Zhenyue; Hongyuan Zha. Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment. SIAM Journal on Scientific Computing. 2005, 26 (1): 313–338. CiteSeerX 10.1.1.211.9957 . doi:10.1137/s1064827502419154.

researchgate.net

Gorban, A. N.; Kégl, B.; Wunsch, D. C.; Zinovyev, A. (编). Principal Manifolds for Data Visualisation and Dimension Reduction. Lecture Notes in Computer Science and Engineering (LNCSE) 58. Springer. 2007. ISBN 978-3-540-73749-0.

sciencedirect.com

Lespinats S., Fertil B., Villemain P. and Herault J., Rankvisu: Mapping from the neighbourhood network, Neurocomputing, vol. 72 (13–15), pp. 2964–2978, 2009.

semanticscholar.org

api.semanticscholar.org

Haller, George; Ponsioen, Sten. Nonlinear normal modes and spectral submanifolds: Existence, uniqueness and use in model reduction. Nonlinear Dynamics. 2016, 86 (3): 1493–1534. S2CID 44074026. arXiv:1602.00560 . doi:10.1007/s11071-016-2974-z.
Schölkopf, B.; Smola, A.; Müller, K.-R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation (MIT Press). 1998, 10 (5): 1299–1319. S2CID 6674407. doi:10.1162/089976698300017467.
Gorban, A. N.; Zinovyev, A. Principal manifolds and graphs in practice: from molecular biology to dynamical systems. International Journal of Neural Systems. 2010, 20 (3): 219–232. PMID 20556849. S2CID 2170982. arXiv:1001.1122 . doi:10.1142/S0129065710002383.
Tenenbaum, J B.; de Silva, V.; Langford, J.C. A Global Geometric Framework for Nonlinear Dimensionality Reduction (PDF). Science. 2000, 290 (5500): 2319–23. Bibcode:2000Sci...290.2319T. PMID 11125149. S2CID 221338160. doi:10.1126/science.290.5500.2319.
Roweis, S. T.; Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science. 2000, 290 (5500): 2323–6. Bibcode:2000Sci...290.2323R. PMID 11125150. S2CID 5987139. doi:10.1126/science.290.5500.2323.
Sidhu, Gagan. Locally Linear Embedding and fMRI feature selection in psychiatric classification. IEEE Journal of Translational Engineering in Health and Medicine. 2019, 7: 1–11. PMC 6726465 . PMID 31497410. S2CID 201832756. arXiv:1908.06319 . doi:10.1109/JTEHM.2019.2936348.
Ding, M.; Fan, G. Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling. IEEE Transactions on Cybernetics. 2015, 45 (11): 2413–24. PMID 25532201. S2CID 15591304. doi:10.1109/TCYB.2014.2373393.
Li, James X. Visualizing high-dimensional data with relational perspective map (PDF). Information Visualization. 2004, 3: 49–59. S2CID 7566939. doi:10.1057/palgrave.ivs.9500051.
Coifman, Ronald R.; Lafon, Stephane. Diffusion Maps (PDF). Applied and Computational Harmonic Analysis. July 2006, 21 (1): 5–30. S2CID 17160669. doi:10.1016/j.acha.2006.04.006.
Rosman, G.; Bronstein, M.M.; Bronstein, A.M.; Kimmel, R. Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding (PDF). International Journal of Computer Vision. 2010, 89 (1): 56–68. S2CID 1365750. doi:10.1007/s11263-010-0322-1.

stanford.edu

web.stanford.edu

Hastie, T.; Stuetzle, W. Principal Curves (PDF). Journal of the American Statistical Association. June 1989, 84 (406): 502–6. doi:10.1080/01621459.1989.10478797.

technion.ac.il

cs.technion.ac.il

Rosman, G.; Bronstein, M.M.; Bronstein, A.M.; Kimmel, R. Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding (PDF). International Journal of Computer Vision. 2010, 89 (1): 56–68. S2CID 1365750. doi:10.1007/s11263-010-0322-1.

ucdavis.edu

math.ucdavis.edu

Coifman, Ronald R.; Lafon, Stephane. Diffusion Maps (PDF). Applied and Computational Harmonic Analysis. July 2006, 21 (1): 5–30. S2CID 17160669. doi:10.1016/j.acha.2006.04.006.

ucl.ac.be

dice.ucl.ac.be

Sun, Jigang; Crowe, Malcolm; Fyfe, Colin. Curvilinear component analysis and Bregman divergences (PDF). European Symposium on Artificial Neural Networks (Esann). d-side publications. 2010: 81–86.

umap-learn.readthedocs.io

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction — umap 0.3 documentation. umap-learn.readthedocs.io. [2019-05-04].

umass.edu

people.cs.umass.edu

Wang, Chang; Mahadevan, Sridhar. Manifold Alignment using Procrustes Analysis (PDF). The 25th International Conference on Machine Learning: 1120–7. July 2008.

upmc.fr

chronos.isir.upmc.fr

Demartines, P.; Hérault, J. Curvilinear Component Analysis: A Self-Organizing Neural Network for Nonlinear Mapping of Data Sets (PDF). IEEE Transactions on Neural Networks. 1997, 8 (1): 148–154. PMID 18255618. doi:10.1109/72.554199.

web.archive.org

Hastie, T. Principal Curves and Surfaces (PDF) (学位论文). Stanford Linear Accelerator Center, Stanford University. November 1984. （原始内容存档 (PDF)于2019-08-02）.
ELastic MAPs. [2016-05-30]. （原始内容存档于2011-07-20）.

worldcat.org

Belkin, Mikhail; Niyogi, Partha. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering (PDF). Advances in Neural Information Processing Systems (MIT Press). 2001, 14: 586–691. ISBN 0-262-27173-7. OCLC 52710683.
DeMers, D.; Cottrell, G.W. Non-linear dimensionality reduction. Advances in neural information processing systems 5. 1993: 580–7. ISBN 1558600159. OCLC 928936290.

yale.edu

search.library.yale.edu

Lafon, Stephane. Diffusion Maps and Geometric Harmonics (学位论文). Yale University. May 2004.