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Multivariate
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#	Item
241	Latent Outlier Detection and the Low Precision Problem Fei Wang, Sanjay Chawla and Didi Surian University of Sydney Add to Reading List Source URL: www.outlier-analytics.org Language: English - Date: 2013-08-13 08:05:46 Linear algebra Matrix theory Multivariate statistics Robust statistics Non-negative matrix factorization Outlier Singular value decomposition
242	Tilburg centre for Creative Computing Tilburg University http://www.uvt.nl/ticc P.O. BoxLE Tilburg, The Netherlands Add to Reading List Source URL: lvdmaaten.github.io Language: English - Date: 2016-07-16 15:30:43 Multivariate statistics Dimension reduction Machine learning Computational statistics Dimension Nonlinear dimensionality reduction Dimensionality reduction Isomap Principal component analysis Kernel principal component analysis Semidefinite embedding Kernel method
243	CS168: The Modern Algorithmic Toolbox Lecture #7: Understanding and Using Principal Component Analysis (PCA) Tim Roughgarden & Gregory Valiant∗ April 18, 2016 Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-06-04 09:49:44 Multivariate statistics Dimension reduction Abstract algebra Linear algebra Analytic geometry Principal component analysis Dimensionality reduction Eigenvalues and eigenvectors Linear regression Euclidean vector Degrees of freedom Eigenface
244	Sliding Tolerance 3-D Point Reduction for Globograms Steven Prashker Cartographic Research Unit Department of Geography Carleton University Ottawa, Ontario, Canada K1S 5B6 Add to Reading List Source URL: mapcontext.com Language: English - Date: 2008-08-30 00:55:15 Reduction Multivariate statistics Map projection Plane Coordinate system Rasterisation Nonlinear dimensionality reduction K-nearest neighbors algorithm
245	Methods in Ecology and Evolution 2015, 6, 412–423 doi: 210XSPECIAL FEATURE NEW OPPORTUNITIES AT THE INTERFACE BETWEEN ECOLOGY AND STATISTICS Add to Reading List Source URL: prism.nacse.org Language: English - Date: 2015-04-13 19:30:57 Regression analysis Linear regression Regression dilution Logistic regression Statistics Prediction Dependent and independent variables Generalized linear model Errors and residuals Observational error Multivariate adaptive regression splines
246	DSP: Robust Semi-Supervised Dimensionality Reduction using Dual Subspace Projections Su Yan∗ Sofien Bouaziz Dongwon Lee IBM Almaden Research Center Ecole Polytechnique F´ed´erale de Lausanne Pennsylvania State Univer Add to Reading List Source URL: sofienbouaziz.com Language: English - Date: 2015-11-19 01:50:34 Multivariate statistics Dimension reduction Geostatistics Functional analysis Linear algebra Dimensionality reduction Principal component analysis Nonlinear dimensionality reduction K-nearest neighbors algorithm Cluster analysis Kernel method Linear discriminant analysis
247	Effective Multi-Modal Retrieval based on Stacked Auto-Encoders Wei Wang† , Beng Chin Ooi† , Xiaoyan Yang‡ , Dongxiang Zhang† , Yueting Zhuang§ †School of Computing, National University of Singapore, Singapore Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2014-02-05 09:31:17 Machine learning Artificial neural networks Multivariate statistics Deep learning Feature learning Autoencoder KeY K-nearest neighbors algorithm Dimensionality reduction
248	Chapter 6 Constructs, Components, and Factor models Parsimony of description has been a goal of science since at least the famous dictum commonly attributed to William of Ockham to not multiply entities beyond necessity Add to Reading List Source URL: personality-project.org Language: English - Date: 2011-08-17 18:07:44 Covariance and correlation Factor analysis Multivariate statistics Regression analysis Linear algebra Principal component analysis Eigenvalues and eigenvectors Covariance matrix Matrix Exploratory factor analysis Singular value decomposition Multiple correlation
249	Algorithms for Non-negative Matrix Factorization Daniel D. Lee* BelJ Laboratories Lucent Technologies Add to Reading List Source URL: papers.nips.cc Language: English* - Date: 2014-04-15 02:01:26 Matrix theory Multivariate statistics Linear algebra Non-negative matrix factorization Matrix Derivative KullbackLeibler divergence Divergence PerronFrobenius theorem Convolution Principal component analysis
250	Tutorial: Using the R Environment for Statistical Computing An example with the Mercer & Hall wheat yield dataset D G Rossiter University of Twente, Faculty of Geo-Information Science & Earth Observation (ITC) Add to Reading List Source URL: www.css.cornell.edu Language: English - Date: 2015-10-21 13:33:25 Statistical theory Regression analysis Econometrics Actuarial science Multivariate statistics R Data analysis Principal component analysis Errors and residuals Spatial analysis Linear regression Bootstrapping