- Referenced in 283 articles
- neighbor interpolation and placement of streamlines), shape analysis, fitting, and distances (smallest enclosing sphere ... spheres, smallest enclosing ellipsoid of points, principal component analysis), and kinetic data structures. All these...
- Referenced in 112 articles
- ways. First, we modify the privacy analysis to real-valued functions f and arbitrary ... very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm...
- Referenced in 70 articles
- Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described ... with applications to sparse principal components and canonical correlation analysis. Biostatistics...
- Referenced in 46 articles
- ROBPCA: A New Approach to Robust Principal Component Analysis. We introduce a new method ... robust principal component analysis (PCA). Classical PCA is based on the empirical covariance matrix...
KPCA plus LDA
- Referenced in 64 articles
- examines the theory of kernel Fisher discriminant analysis (KFD) in a Hilbert space and develops ... phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis...
- Referenced in 66 articles
- smoothed weighted histograms, we apply Principal Components Analysis (PCA) to the normalized gradient patch...
- Referenced in 44 articles
- symbolic description of groups, factorial discriminant analysis, principal component analysis, histograms and elementary statistics, segmentation...
- Referenced in 48 articles
- common set of items and applies Principal Component Analysis (PCA) to the resulting dense subset...
- Referenced in 24 articles
- LIBRA. LIBRA: a MATLAB Library for Robust Analysis is developed at ROBUST@Leuven, the research ... estimation (MCD), regression (LTS, MCD-regression), Principal Component Analysis (RAPCA, ROBPCA), Principal Component Regression (RPCR...
- Referenced in 25 articles
- Problems, including the n-Way Parallel Factor Analysis Model. A technique for fitting multilinear ... many-dimensional data arrays is described. Principal component analysis and three-way PARAFAC factor analysis...
- Referenced in 37 articles
- package survey: analysis of complex survey samples. Summary statistics, two-sample tests, generalised linear models ... standardization. PPS sampling without replacement. Principal components, factor analysis...
- Referenced in 20 articles
- piecewise continuous manifold. The local principal component analysis algorithm is used for building such...
- Referenced in 27 articles
- data using principal components. Functional principal components (FPC) analysis is widely used to decompose ... implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably...
- Referenced in 16 articles
- simulations, use statistical plots for exploratory data analysis, and perform hypothesis tests. For analyzing multidimensional ... feature selection, transform your data with principal component analysis, apply regularization and shrinkage...
- Referenced in 11 articles
- statistics and plots (ternary diagrams, boxplots), principal component analysis (using biplots), cluster analysis with Aitchison...
- Referenced in 7 articles
- Handling missing values with/in multivariate data analysis (principal component methods). Imputation of incomplete continuous ... Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis...
- Referenced in 9 articles
- Bayesian posterior sample); conducting an analysis called stochastic character mapping, in which character histories ... response variables; conducting a phylogenetic principal components analysis, a phylogenetic regression, a reduced major axis...
- Referenced in 7 articles
- include: sparse PLS-Discriminant Analysis, Independent Principal Component Analysis and multilevel analysis using variance decomposition...
- Referenced in 5 articles
- implementation of a randomized algorithm for principal component analysis. Recent years have witnessed intense development ... rank approximation. These methods target principal component analysis and the calculation of truncated singular value...
- Referenced in 6 articles
- shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate...