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Principal component analysis
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#	Item
41	Interactive comment on “Predicting East African spring droughts using Paciﬁc and Indian Ocean sea surface temperature indices” by C. Funk et al. Anonymous Referee #1 Received and published: 23 April 2014 The paper Add to Reading List Source URL: www.hydrol-earth-syst-sci-discuss.net Language: English - Date: 2016-01-11 03:11:36 Atmospheric sciences Covariance and correlation Physical geography Meteorology Climate Climate as complex networks Network theory Correlation and dependence Correlation Principal component analysis Teleconnection
42	DOI: j01783.x COMPUTER GRAPHICS forum Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2013-10-23 08:31:15 Algebra Mathematics Linear algebra Multivariate statistics Matrix theory Dimension reduction Interpolation Principal component analysis Eigenvalues and eigenvectors Kernel principal component analysis Eigendecomposition of a matrix Radial basis function
43	Source Separation as a By-Product of Regularization Sepp Hochreiter Fakultat fur Informatik Add to Reading List Source URL: www.bioinf.jku.at Language: English - Date: 2013-01-23 02:19:45 Statistics Multivariate statistics Artificial neural networks Applied mathematics Machine learning Dimension reduction Signal processing Independent component analysis Principal component analysis Factorial code Pattern recognition Self-organizing map
44	Linear Subspace Design for Real-Time Shape Deformation Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2016-05-19 11:59:40 Algebra Mathematics Linear algebra Finite element method Eigenvalues and eigenvectors Vector space Linear map Principal component analysis Linear subspace Plasticity Singular value decomposition Deformation
45	Learning Spatially-Smooth Mappings in Non-Rigid Structure from Motion Onur C. Hamsici1 , Paulo F.U. Gotardo2 , and Aleix M. Martinez2 1 Qualcomm Research, San Diego, CA, USA Add to Reading List Source URL: www2.ece.ohio-state.edu Language: English - Date: 2012-12-12 10:55:54 Algebra Mathematics Linear algebra Functional analysis Numerical linear algebra Abstract algebra Matrix theory Matrix Kernel Linear map Eigenvalues and eigenvectors Principal component analysis
46	Data-Intensive Statistical Challenges in Astrophysics Alex Szalay The Johns Hopkins University Collaborators: T. Budavari, C-W Yip (JHU), Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-03-09 01:30:23 Astronomy Astronomical surveys Observational astronomy Physical cosmology Alfred P. Sloan Foundation Sloan Digital Sky Survey Redshift survey Principal component analysis The Dark Energy Survey 2dF Galaxy Redshift Survey Alex Szalay Redshift
47	Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development Diane J. Hu1 , Laurens van der Maaten1,2 , Youngmin Cho1 , Lawrence K. Saul1 , Sorin Lerner1 1 Dept. of Computer Science & Engin Add to Reading List Source URL: lvdmaaten.github.io Language: English - Date: 2016-07-16 15:30:43 Software Probability Statistics Archive formats Dimension reduction Principal component analysis Mixture model Apache Subversion Markov chain Restricted Boltzmann machine Version control Tar
48	Latent Outlier Detection and the Low Precision Problem Fei Wang Sanjay Chawla Didi Surian Add to Reading List Source URL: www.outlier-analytics.org Language: English - Date: 2013-07-04 03:56:20 Algebra Mathematics Statistics Linear algebra Multivariate statistics Matrix theory Data mining Functional analysis Non-negative matrix factorization Outlier Principal component analysis Anomaly detection
49	JMLR: Workshop and Conference Proceedings vol 40:1–51, 2015 Tensor principal component analysis via sum-of-squares proofs Sam Hopkins Jonathan Shi David Steurer Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-07-20 20:08:36 Algebra Mathematics Linear algebra Multivariate statistics Matrix theory Tensors Real algebraic geometry Non-negative matrix factorization Matrix Higher-order singular value decomposition Eigenvalues and eigenvectors Principal component analysis
50	A MDL4BMF: Minimum Description Length for Boolean Matrix Factorization PAULI MIETTINEN, Max-Planck Institute for Informatics JILLES VREEKEN, Max-Planck Institute for Informatics, Saarland University, University of Antwer Add to Reading List Source URL: people.mpi-inf.mpg.de Language: English - Date: 2014-05-09 10:45:37 Algebra Mathematics Linear algebra Matrix theory Numerical linear algebra Multivariate statistics Dimension reduction Non-negative matrix factorization Matrix Singular value decomposition Principal component analysis Factorization

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