Academic literature on the topic 'Active subspace'
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Journal articles on the topic "Active subspace"
Holodnak, John T., Ilse C. F. Ipsen, and Ralph C. Smith. "A Probabilistic Subspace Bound with Application to Active Subspaces." SIAM Journal on Matrix Analysis and Applications 39, no. 3 (January 2018): 1208–20. http://dx.doi.org/10.1137/17m1141503.
Full textXie, Ziqi, and Lihong Wang. "Active Block Diagonal Subspace Clustering." IEEE Access 9 (2021): 83976–92. http://dx.doi.org/10.1109/access.2021.3087575.
Full textYu, Yu Min. "The Characteristics of Affine Bivariate Pseudoframes of Subspace Associated with a Bivariate Filter Functions." Key Engineering Materials 439-440 (June 2010): 926–31. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.926.
Full textLi, Changsheng, Kaihang Mao, Lingyan Liang, Dongchun Ren, Wei Zhang, Ye Yuan, and Guoren Wang. "Unsupervised Active Learning via Subspace Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8332–39. http://dx.doi.org/10.1609/aaai.v35i9.17013.
Full textXu, Yong Fan. "Study of Matrix Multipliers for Normalized Frame Multi-Wavelets and Applications in Engineering Material Technology." Advanced Materials Research 753-755 (August 2013): 2321–24. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2321.
Full textErdal, Daniel, and Olaf A. Cirpka. "Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces." Hydrology and Earth System Sciences 23, no. 9 (September 18, 2019): 3787–805. http://dx.doi.org/10.5194/hess-23-3787-2019.
Full textLiu, Yanbei, Kaihua Liu, Changqing Zhang, Xiao Wang, Shaona Wang, and Zhitao Xiao. "Entropy-based active sparse subspace clustering." Multimedia Tools and Applications 77, no. 17 (April 20, 2018): 22281–97. http://dx.doi.org/10.1007/s11042-018-5945-1.
Full textSeshadri, P., S. Yuchi, G. T. Parks, and S. Shahpar. "Supporting multi-point fan design with dimension reduction." Aeronautical Journal 124, no. 1279 (July 27, 2020): 1371–98. http://dx.doi.org/10.1017/aer.2020.50.
Full textLiu, Guangcan, and Shuicheng Yan. "Active Subspace: Toward Scalable Low-Rank Learning." Neural Computation 24, no. 12 (December 2012): 3371–94. http://dx.doi.org/10.1162/neco_a_00369.
Full textN., Navaneeth, and Souvik Chakraborty. "Surrogate assisted active subspace and active subspace assisted surrogate—A new paradigm for high dimensional structural reliability analysis." Computer Methods in Applied Mechanics and Engineering 389 (February 2022): 114374. http://dx.doi.org/10.1016/j.cma.2021.114374.
Full textDissertations / Theses on the topic "Active subspace"
Zhu, Weizhong Allen Robert B. "Text clustering and active learning using a LSI subspace signature model and query expansion /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3077.
Full textNijsse, Gerard. "A subspace based approach to the design, implementation and validation of algorithms for active vibration isolation control." Enschede : University of Twente [Host], 2006. http://doc.utwente.nl/51108.
Full textOnder, Murat. "Face Detection And Active Robot Vision." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605290/index.pdf.
Full textAguiar, Izabel Pirimai. "Dynamic Active Subspaces| A Data-driven Approach to Computing Time-dependent Active Subspaces in Dynamical Systems." Thesis, University of Colorado at Boulder, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10826096.
Full textComputational models are aiding in the advancement of science – from biological, to engineering, to social systems. To trust the predictions of computational models, however, we must understand how the errors in the models’ inputs (i.e., through measurement error) affect the output of the systems: we must quantify the uncertainty that results from these input errors. Uncertainty quantification (UQ) becomes computationally complex when there are many parameters in the model. In such cases it is useful to reduce the dimension of the problem by identifying unimportant parameters and disregarding them for UQ studies. This makes an otherwise intractable UQ problem tractable. Active subspaces extend this idea by identifying important linear combinations of parameters, enabling more powerful and effective dimension reduction. Although active subspaces give model insight and computational tractability for scalar-valued functions, it is not enough. This analysis does not extend to time-dependent systems. In this thesis we discuss time-dependent, dynamic active subspaces. We develop a methodology by which to compute and approximate dynamic active subspaces, and introduce the analytical form of dynamic active subspaces for two cases. To highlight these methods we find dynamic active subspaces for a linear harmonic oscillator and a nonlinear enzyme kinetics system.
Lund, Kathryn. "A new block Krylov subspace framework with applications to functions of matrices acting on multiple vectors." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/493337.
Full textPh.D.
We propose a new framework for understanding block Krylov subspace methods, which hinges on a matrix-valued inner product. We can recast the ``classical" block Krylov methods, such as O'Leary's block conjugate gradients, global methods, and loop-interchange methods, within this framework. Leveraging the generality of the framework, we develop an efficient restart procedure and error bounds for the shifted block full orthogonalization method (Sh-BFOM(m)). Regarding BFOM as the prototypical block Krylov subspace method, we propose another formalism, which we call modified BFOM, and show that block GMRES and the new block Radau-Lanczos method can be regarded as modified BFOM. In analogy to Sh-BFOM(m), we develop an efficient restart procedure for shifted BGMRES with restarts (Sh-BGMRES(m)), as well as error bounds. Using this framework and shifted block Krylov methods with restarts as a foundation, we formulate block Krylov subspace methods with restarts for matrix functions acting on multiple vectors f(A)B. We obtain convergence bounds for \bfomfom (BFOM for Functions Of Matrices) and block harmonic methods (i.e., BGMRES-like methods) for matrix functions. With various numerical examples, we illustrate our theoretical results on Sh-BFOM and Sh-BGMRES. We also analyze the matrix polynomials associated to the residuals of these methods. Through a variety of real-life applications, we demonstrate the robustness and versatility of B(FOM)^2 and block harmonic methods for matrix functions. A particularly interesting example is the tensor t-function, our proposed definition for the function of a tensor in the tensor t-product formalism. Despite the lack of convergence theory, we also show that the block Radau-Lanczos modification can reduce the number of cycles required to converge for both linear systems and matrix functions.
Temple University--Theses
Teixeira, Parente Mario Manuel [Verfasser], Barbara [Akademischer Betreuer] Wohlmuth, Barbara [Gutachter] Wohlmuth, and Krzysztof [Gutachter] Podgórski. "Active Subspaces in Bayesian Inverse Problems / Mario Manuel Teixeira Parente ; Gutachter: Barbara Wohlmuth, Krzysztof Podgórski ; Betreuer: Barbara Wohlmuth." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1220319333/34.
Full textLund, Kathryn [Verfasser]. "A new block Krylov subspace framework with applications to functions of matrices acting on multiple vectors / Kathryn Lund." Wuppertal : Universitätsbibliothek Wuppertal, 2018. http://d-nb.info/1164098926/34.
Full textXue, Cheng. "Improve the Active Subspace Method by Partitioning the Parameter Space." Master's thesis, 2018. http://hdl.handle.net/1885/173619.
Full text(8734437), Rohit Tripathy. "Surrogate Modeling for Uncertainty Quantification in systems Characterized by expensive and high-dimensional numerical simulators." Thesis, 2020.
Find full textBooks on the topic "Active subspace"
Constantine, Paul G. Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies. SIAM-Society for Industrial and Applied Mathematics, 2015.
Find full textBook chapters on the topic "Active subspace"
Joshi, Vineet, and Raj Bhatnagar. "eSelect: Effective Subspace Selection for Detection of Anomalies." In Active Media Technology, 251–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09912-5_21.
Full textJoshi, Vineet, and Raj Bhatnagar. "Outlier Analysis Using Lattice of Contiguous Subspaces." In Active Media Technology, 238–50. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09912-5_20.
Full textJia, Chengcheng, and Yun Fu. "Subspace Learning for Action Recognition." In Human Activity Recognition and Prediction, 49–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_3.
Full textOreifej, Omar, and Mubarak Shah. "Action Recognition by Motion Trajectory Decomposition." In Robust Subspace Estimation Using Low-Rank Optimization, 55–67. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04184-1_5.
Full textBeleza, Suzana R. A., and Kazuhiro Fukui. "Slow Feature Subspace for Action Recognition." In Pattern Recognition. ICPR International Workshops and Challenges, 702–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68796-0_51.
Full textPaternain, Gabriel P. "The Geodesic Flow Acting on Lagrangian Subspaces." In Geodesic Flows, 31–50. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1600-1_3.
Full textLuong, Vinh D., Lipo Wang, and Gaoxi Xiao. "Action Recognition Using Hierarchical Independent Subspace Analysis with Trajectory." In Proceedings in Adaptation, Learning and Optimization, 549–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13359-1_42.
Full textXu, Lu, Xian Zhong, Wenxuan Liu, Shilei Zhao, Zhengwei Yang, and Luo Zhong. "Subspace Enhancement and Colorization Network for Infrared Video Action Recognition." In PRICAI 2021: Trends in Artificial Intelligence, 321–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89370-5_24.
Full textTezzele, Marco, Francesco Ballarin, and Gianluigi Rozza. "Combined Parameter and Model Reduction of Cardiovascular Problems by Means of Active Subspaces and POD-Galerkin Methods." In SEMA SIMAI Springer Series, 185–207. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96649-6_8.
Full textKairies, Hans-Heinrich. "Properties of an Operator Acting on the Space of Bounded Real Functions and Certain Subspaces." In Functional Equations — Results and Advances, 175–86. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5288-5_13.
Full textConference papers on the topic "Active subspace"
Seshadri, Pranay, Shahrokh Shahpar, Paul Constantine, Geoffrey Parks, and Mike Adams. "Turbomachinery Active Subspace Performance Maps." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64528.
Full textLeon, Lider S., Ralph C. Smith, William S. Oates, and Paul Miles. "Identifiability and Active Subspace Analysis for a Polydomain Ferroelectric Phase Field Model." In ASME 2017 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/smasis2017-3845.
Full textXiaofei He and Deng Cai. "Active subspace learning." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459329.
Full textLipor, John, and Laura Balzano. "Margin-based active subspace clustering." In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383815.
Full textPeng, Hankui, and Nicos G. Pavlidis. "Subspace Clustering with Active Learning." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006361.
Full textTripathy, Rohit, and Ilias Bilionis. "Deep Active Subspaces: A Scalable Method for High-Dimensional Uncertainty Propagation." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98099.
Full textBeck, Joseph A., Jeffrey M. Brown, Alex A. Kaszynski, and Emily B. Carper. "Active Subspace Development of Integrally Bladed Disk Dynamic Properties due to Manufacturing Variations." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76800.
Full textBiao Niu, Yifan Zhang, Jinqiao Wang, Jian Cheng, and Hanqing Lu. "Subspace learning based active learning for image retrieval." In 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2013. http://dx.doi.org/10.1109/icmew.2013.6618268.
Full textXie, Ziqi, and Lihong Wang. "Active Structure Learning for Block Diagonal Subspace Clustering." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263491.
Full textPongpairoj, Harin, Vineet Chaparala, and Farzad Pourboghrat. "Real-Time Subspace Identification and Optimal Control for Active Noise Cancellation." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59894.
Full textReports on the topic "Active subspace"
Wang, Qiqi. Active Subspace Methods for Data-Intensive Inverse Problems. Office of Scientific and Technical Information (OSTI), April 2017. http://dx.doi.org/10.2172/1353429.
Full textConstantine, Paul. Active Subspace Methods for Data-Intensive Inverse Problems. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1566065.
Full textBui-Thanh, Tan. Active Subspace Methods for Data-Intensive Inverse Problems (Final Report). Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1494035.
Full textWilliams, Brian J., Kayla Coleman, Ralph C. Smith, and Max D. Morris. Gradient-Free Construction of Active Subspaces for Dimension Reduction. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1523205.
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