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Artykuły w czasopismach na temat "Dimensionality reduction"
Cheng, Long, Chenyu You i Yani Guan. "Random Projections for Non-linear Dimensionality Reduction". International Journal of Machine Learning and Computing 6, nr 4 (sierpień 2016): 220–25. http://dx.doi.org/10.18178/ijmlc.2016.6.4.601.
Pełny tekst źródłaMarchette, David J., i Wendy L. Poston. "Local dimensionality reduction". Computational Statistics 14, nr 4 (12.09.1999): 469–89. http://dx.doi.org/10.1007/s001800050026.
Pełny tekst źródłaSun, Yu-Yin, Michael Ng i Zhi-Hua Zhou. "Multi-Instance Dimensionality Reduction". Proceedings of the AAAI Conference on Artificial Intelligence 24, nr 1 (3.07.2010): 587–92. http://dx.doi.org/10.1609/aaai.v24i1.7700.
Pełny tekst źródłaKoren, Y., i L. Carmel. "Robust linear dimensionality reduction". IEEE Transactions on Visualization and Computer Graphics 10, nr 4 (lipiec 2004): 459–70. http://dx.doi.org/10.1109/tvcg.2004.17.
Pełny tekst źródłaLotlikar, R., i R. Kothari. "Fractional-step dimensionality reduction". IEEE Transactions on Pattern Analysis and Machine Intelligence 22, nr 6 (czerwiec 2000): 623–27. http://dx.doi.org/10.1109/34.862200.
Pełny tekst źródłaGottlieb, Lee-Ad, Aryeh Kontorovich i Robert Krauthgamer. "Adaptive metric dimensionality reduction". Theoretical Computer Science 620 (marzec 2016): 105–18. http://dx.doi.org/10.1016/j.tcs.2015.10.040.
Pełny tekst źródłaPang, Rich, Benjamin J. Lansdell i Adrienne L. Fairhall. "Dimensionality reduction in neuroscience". Current Biology 26, nr 14 (lipiec 2016): R656—R660. http://dx.doi.org/10.1016/j.cub.2016.05.029.
Pełny tekst źródłaLovaglio, Pietro Giorgio, i Giorgio Vittadini. "Multilevel dimensionality-reduction methods". Statistical Methods & Applications 22, nr 2 (27.09.2012): 183–207. http://dx.doi.org/10.1007/s10260-012-0215-2.
Pełny tekst źródłaCarter, Kevin, Raviv Raich, William Finn i Alfred Hero,III. "Information-Geometric Dimensionality Reduction". IEEE Signal Processing Magazine 28, nr 2 (marzec 2011): 89–99. http://dx.doi.org/10.1109/msp.2010.939536.
Pełny tekst źródłaGonen, Mehmet. "Bayesian Supervised Dimensionality Reduction". IEEE Transactions on Cybernetics 43, nr 6 (grudzień 2013): 2179–89. http://dx.doi.org/10.1109/tcyb.2013.2245321.
Pełny tekst źródłaRozprawy doktorskie na temat "Dimensionality reduction"
Ariu, Kaito. "Online Dimensionality Reduction". Licentiate thesis, KTH, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290791.
Pełny tekst źródłaDenna avhandling studerar algoritmer för datareduktion som lär sig från sekventiellt inhämtad data. Vi fokuserar speciellt på frågeställningar som uppkommer i utvecklingen av rekommendationssystem och i identifieringen av heterogena grupper av användare från data. För rekommendationssystem betraktar vi ett system med m användare och n objekt. I varje runda observerar algoritmen en slumpmässigt vald användare och rekommenderar ett objekt. En viktig begränsning i vår problemformuleringar att rekommendationer inte får upprepas: samma objekt inte kan rekommenderas till samma användartermer än en gång. Vi betraktar problemet som en variant av det flerarmadebanditproblemet och analyserar systemprestanda i termer av "ånger” under olika antaganden.Vi härleder fundamentala gränser för ånger och föreslår algoritmer som är (ordningsmässigt) optimala. En intressant komponent av vår analys är att vi lyckas att karaktärisera hur vart och ett av våra antaganden påverkar systemprestandan. T.ex. kan vi kvantifiera prestandaförlusten i ånger på grund av att rekommendationer inte får upprepas, på grund avatt vi måste lära oss statistiken för vilka objekt en användare är intresserade av, och för kostnaden för att lära sig den lågdimensionella rymden för användare och objekt. För problemet med hur man bäst identifierar grupper av användare härleder vi fundamentala gränser för hur snabbt det går att identifiera kluster. Vi gör detta för algoritmer som har samtidig tillgång till all data och för algoritmer som måste lära sig genom sekventiell inhämtning av data. Med tillgång till all data kan vår algoritm uppnå den optimala prestandan ordningsmässigt. När data måste inhämtas sekventiellt föreslår vi en algoritm som är inspirerad av den nedre gränsen på vad som kan uppnås. För båda problemen utvärderar vi de föreslagna algoritmerna numeriskt och jämför den praktiska prestandan med de teoretiska garantierna.
QC 20210223
LEGRAMANTI, SIRIO. "Bayesian dimensionality reduction". Doctoral thesis, Università Bocconi, 2021. http://hdl.handle.net/11565/4035711.
Pełny tekst źródłaWe are currently witnessing an explosion in the amount of available data. Such growth involves not only the number of data points but also their dimensionality. This poses new challenges to statistical modeling and computations, thus making dimensionality reduction more central than ever. In the present thesis, we provide methodological, computational and theoretical advancements in Bayesian dimensionality reduction via novel structured priors. Namely, we develop a new increasing shrinkage prior and illustrate how it can be employed to discard redundant dimensions in Gaussian factor models. In order to make it usable for larger datasets, we also investigate variational methods for posterior inference under this proposed prior. Beyond traditional models and parameter spaces, we also provide a different take on dimensionality reduction, focusing on community detection in networks. For this purpose, we define a general class of Bayesian nonparametric priors that encompasses existing stochastic block models as special cases and includes promising unexplored options. Our Bayesian approach allows for a natural incorporation of node attributes and facilitates uncertainty quantification as well as model selection.
Baldiwala, Aliakbar. "Dimensionality Reduction for Commercial Vehicle Fleet Monitoring". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38330.
Pełny tekst źródłaBolelli, Maria Virginia. "Diffusion Maps for Dimensionality Reduction". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18246/.
Pełny tekst źródłaKhosla, Nitin, i n/a. "Dimensionality Reduction Using Factor Analysis". Griffith University. School of Engineering, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20061010.151217.
Pełny tekst źródłaVamulapalli, Harika Rao. "On Dimensionality Reduction of Data". ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1211.
Pełny tekst źródłaWidemann, David P. "Dimensionality reduction for hyperspectral data". College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8448.
Pełny tekst źródłaThesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Khosla, Nitin. "Dimensionality Reduction Using Factor Analysis". Thesis, Griffith University, 2006. http://hdl.handle.net/10072/366058.
Pełny tekst źródłaThesis (Masters)
Master of Philosophy (MPhil)
School of Engineering
Full Text
Sætrom, Jon. "Reduction of Dimensionality in Spatiotemporal Models". Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11247.
Pełny tekst źródłaGhodsi, Boushehri Ali. "Nonlinear Dimensionality Reduction with Side Information". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/1020.
Pełny tekst źródłaThis thesis makes a number of contributions. The first is a technique for combining different embedding objectives, which is then exploited to incorporate side information expressed in terms of transformation invariants known to hold in the data. It also introduces two different ways of incorporating transformation invariants in order to make new similarity measures. Two algorithms are proposed which learn metrics based on different types of side information. These learned metrics can then be used in subsequent embedding methods. Finally, it introduces a manifold learning algorithm that is useful when applied to sequential decision problems. In this case we are given action labels in addition to data points. Actions in the manifold learned by this algorithm have meaningful representations in that they are represented as simple transformations.
Książki na temat "Dimensionality reduction"
Lee, John A., i Michel Verleysen, red. Nonlinear Dimensionality Reduction. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-39351-3.
Pełny tekst źródłaLespinats, Sylvain, Benoit Colange i Denys Dutykh. Nonlinear Dimensionality Reduction Techniques. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81026-9.
Pełny tekst źródłaGarzon, Max, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana i Lih-Yuan Deng, red. Dimensionality Reduction in Data Science. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9.
Pełny tekst źródłaPaul, Arati, i Nabendu Chaki. Dimensionality Reduction of Hyperspectral Imagery. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-42667-4.
Pełny tekst źródłaStrange, Harry, i Reyer Zwiggelaar. Open Problems in Spectral Dimensionality Reduction. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03943-5.
Pełny tekst źródłaKramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38652-7.
Pełny tekst źródłaKramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Znajdź pełny tekst źródłaShaw, Blake. Graph Embedding and Nonlinear Dimensionality Reduction. [New York, N.Y.?]: [publisher not identified], 2011.
Znajdź pełny tekst źródłaGhojogh, Benyamin, Mark Crowley, Fakhri Karray i Ali Ghodsi. Elements of Dimensionality Reduction and Manifold Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-10602-6.
Pełny tekst źródłaWang, Jianzhong. Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27497-8.
Pełny tekst źródłaCzęści książek na temat "Dimensionality reduction"
Herrera, Francisco, Francisco Charte, Antonio J. Rivera i María J. del Jesus. "Dimensionality Reduction". W Multilabel Classification, 115–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_7.
Pełny tekst źródłaKramer, Oliver. "Dimensionality Reduction". W Dimensionality Reduction with Unsupervised Nearest Neighbors, 33–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38652-7_4.
Pełny tekst źródłaHull, Isaiah. "Dimensionality Reduction". W Machine Learning for Economics and Finance in TensorFlow 2, 281–306. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6373-0_8.
Pełny tekst źródłaShen, Heng Tao. "Dimensionality Reduction". W Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_551-2.
Pełny tekst źródłaWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander i in. "Dimensionality Reduction". W Encyclopedia of Machine Learning, 274–79. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_216.
Pełny tekst źródłaDinov, Ivo D. "Dimensionality Reduction". W Data Science and Predictive Analytics, 233–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72347-1_6.
Pełny tekst źródłaShen, Heng Tao. "Dimensionality Reduction". W Encyclopedia of Database Systems, 843–46. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_551.
Pełny tekst źródłaMathar, Rudolf, Gholamreza Alirezaei, Emilio Balda i Arash Behboodi. "Dimensionality Reduction". W Fundamentals of Data Analytics, 45–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56831-3_4.
Pełny tekst źródłaDurstewitz, Daniel. "Dimensionality Reduction". W Advanced Data Analysis in Neuroscience, 105–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59976-2_6.
Pełny tekst źródłaBraga-Neto, Ulisses. "Dimensionality Reduction". W Fundamentals of Pattern Recognition and Machine Learning, 205–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27656-0_9.
Pełny tekst źródłaStreszczenia konferencji na temat "Dimensionality reduction"
Bunte, Kerstin, Michael Biehl i Barbara Hammer. "Dimensionality reduction mappings". W 2011 Ieee Symposium On Computational Intelligence And Data Mining - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cidm.2011.5949443.
Pełny tekst źródłaSchclar, Alon, i Amir Averbuch. "Diffusion Bases Dimensionality Reduction". W 7th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005625301510156.
Pełny tekst źródłaBingham, Ella, Aristides Gionis, Niina Haiminen, Heli Hiisilä, Heikki Mannila i Evimaria Terzi. "Segmentation and dimensionality reduction". W Proceedings of the 2006 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.33.
Pełny tekst źródłaZhang, Daoqiang, Zhi-Hua Zhou i Songcan Chen. "Semi-Supervised Dimensionality Reduction". W Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.73.
Pełny tekst źródłaGuo, Ce, i Wayne Luk. "Quantisation-aware Dimensionality Reduction". W 2020 International Conference on Field-Programmable Technology (ICFPT). IEEE, 2020. http://dx.doi.org/10.1109/icfpt51103.2020.00041.
Pełny tekst źródłaZhu, Xiaofeng, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan i Shichao Zhang. "Robust Graph Dimensionality Reduction". W Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/452.
Pełny tekst źródłaGashler, Mike, i Tony Martinez. "Temporal nonlinear dimensionality reduction". W 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033465.
Pełny tekst źródłaHeylen, Rob, i Paul Scheunders. "Nonlinear barycentric dimensionality reduction". W 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5653675.
Pełny tekst źródłaMosci, Sofia, Lorenzo Rosasco i Alessandro Verri. "Dimensionality reduction and generalization". W the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273579.
Pełny tekst źródłaLuo, Xianghui, i Robert J. Durrant. "Maximum Gradient Dimensionality Reduction". W 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546198.
Pełny tekst źródłaRaporty organizacyjne na temat "Dimensionality reduction"
Jain, Anil K. Classification, Clustering and Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2008. http://dx.doi.org/10.21236/ada483446.
Pełny tekst źródłaWolf, Lior, i Stanley Bileschi. Combining Variable Selection with Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, marzec 2005. http://dx.doi.org/10.21236/ada454990.
Pełny tekst źródłaJones, Michael J. Using Recurrent Networks for Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1992. http://dx.doi.org/10.21236/ada259497.
Pełny tekst źródłaLeón, Carlos. Detecting anomalous payments networks: A dimensionality reduction approach. Banco de la República de Colombia, grudzień 2019. http://dx.doi.org/10.32468/be.1098.
Pełny tekst źródłaSarwar, Badrul, George Karypis, Joseph Konstan i John Riedl. Application of Dimensionality Reduction in Recommender System - A Case Study. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2000. http://dx.doi.org/10.21236/ada439541.
Pełny tekst źródłaFukumizu, Kenji, Francis R. Bach i Michael I. Jordan. Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces. Fort Belvoir, VA: Defense Technical Information Center, maj 2003. http://dx.doi.org/10.21236/ada446572.
Pełny tekst źródłaNichols, Jonathan M., Frank Bucholtz i Joseph V. Michalowicz. Intelligent Data Fusion Using Sparse Representations and Nonlinear Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2009. http://dx.doi.org/10.21236/ada507109.
Pełny tekst źródłaVales, C., Y. Choi, D. Copeland i S. Cheung. Energy conserving quadrature based dimensionality reduction for nonlinear hydrodynamics problems. Office of Scientific and Technical Information (OSTI), sierpień 2023. http://dx.doi.org/10.2172/1995059.
Pełny tekst źródłaHo, Tu Bao. Methods of Sparse Modeling and Dimensionality Reduction to Deal with Big Data. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 2015. http://dx.doi.org/10.21236/ada623178.
Pełny tekst źródłaMohan, Anish, Guillermo Sapiro i Edward Bosch. Spatially-Coherent Non-Linear Dimensionality Reduction and Segmentation of Hyper-Spectral Images (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2006. http://dx.doi.org/10.21236/ada478496.
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