Kernel-Based Contrast Functions for Sufficient Dimension Reduction K. Fukumizu, F. Bach, & M. I. Jordan, (Annals of Statistics, 37, .

Source URL: mlg.eng.cam.ac.uk

Sufficient Dimension Reduction via Bayesian Mixture Modeling Brian J. Reich1 , Howard D. Bondell, and Lexin Li Department of Statistics, North Carolina State University, Raleigh, NC August 6, 2010 Summary.

Source URL: www4.stat.ncsu.edu

Index active subspace analytic examples, 25 choosing the dimension, 6, 37, 59 computing, 25–35

Source URL: www.siam.org

• Multivariate statistics
• Singular value decomposition
• Mathematical optimization
• Jet engines
• Spacecraft propulsion
• HyShot
• Scramjet
• Sufficient dimension reduction
• Principal component analysis
• Algebra
• Statistics
• Mathematics

30th August, Luke Prendergast, La Trobe University

Source URL: www.statsoc.org.au

• Linear regression
• Least squares
• Degrees of freedom
• Sufficient dimension reduction
• Statistics
• Regression analysis
• Sliced inverse regression

Joint Statistics Seminar The Hong Kong University of Science and Technology Directional Regression for Dimension Reduction by

Source URL: www.bm.ust.hk

• Dimension reduction
• Regression
• Degrees of freedom
• Sufficient dimension reduction
• Statistics
• Regression analysis
• Sliced inverse regression

Multinomial Inverse Regression for Text Analysis Matt Taddy arXiv:1012.2098v7 [stat.ME] 8 Aug 2013

Source URL: arxiv.org

• Sufficient dimension reduction
• Sliced inverse regression
• Multinomial logit
• Linear regression
• Separation
• Multinomial distribution
• Exponential family
• Generalized linear model
• Outline of regression analysis
• Statistics
• Regression analysis
• Logistic regression

Regression on Manifolds Using Kernel Dimension Reduction Jens Nilsson Centre for Mathematical Sciences, Lund University, Box 118, SE[removed]Lund, Sweden Fei Sha Computer Science Division, University of California, Berke

Source URL: www-bcf.usc.edu

• Multivariate statistics
• Nonlinear dimensionality reduction
• Dimension reduction
• Principal component analysis
• Sufficient dimension reduction
• Manifold
• Eigenvalues and eigenvectors
• Projection
• Linear discriminant analysis
• Statistics
• Mathematics
• Algebra

Unsupervised Kernel Dimension Reduction Meihong Wang Dept. of Computer Science U. of Southern California Los Angeles, CA 90089

Source URL: www-bcf.usc.edu

• Dimension reduction
• Nonlinear dimensionality reduction
• Kernel principal component analysis
• Principal component analysis
• Supervised learning
• Sliced inverse regression
• Sufficient dimension reduction
• Gaussian function
• Projection
• Statistics
• Mathematics
• Multivariate statistics

Weighted Principal Support Vector Machines for Sufficient Dimension Reduction in Binary Classification

Source URL: srcos2014.blogs.rice.edu

• Regression analysis
• Singular value decomposition
• Abstract algebra
• Multivariate statistics
• Sliced inverse regression
• Sufficient dimension reduction
• Kernel
• Support vector machine
• Vector space
• Algebra
• Mathematics
• Linear algebra

Dimension reduction and visualization of histogram data using sliced inverse regression Han-Ming Wu1,* 1. Department of Mathematics, Tamkang University *Contact author: [removed]

Source URL: www3.stat.sinica.edu.tw

• Data analysis
• Non-parametric statistics
• Principal component analysis
• Singular value decomposition
• Sufficient dimension reduction
• Sliced inverse regression
• Dimension reduction
• Histogram
• Data visualization
• Statistics
• Multivariate statistics
• Regression analysis