• ROBPCA

  • Referenced in 68 articles [sw11592]
  • 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 ... highly sensitive to outlying observations. Two robust approaches have been developed to date. The first...
  • robCompositions

  • Referenced in 13 articles [sw11804]
  • outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis...
  • Pyglrm

  • Referenced in 26 articles [sw27003]
  • data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative matrix factorization...
  • LowRankModels

  • Referenced in 27 articles [sw27002]
  • data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative matrix factorization...
  • rsvd

  • Referenced in 7 articles [sw16104]
  • widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized ... function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions...
  • PCA-SIFT

  • Referenced in 78 articles [sw04592]
  • smoothed weighted histograms, we apply Principal Components Analysis (PCA) to the normalized gradient patch ... based local descriptors are more distinctive, more robust to image deformations, and more compact than...
  • Gmedian

  • Referenced in 2 articles [sw21318]
  • Matrix with Application to Online Robust Principal Components Analysis. The geometric median covariation matrix ... weak conditions. The computation of the principal components can also be performed online and this ... data is small and robust principal components analysis based on projection pursuit and spherical projections ... interest of considering the robust principal components analysis based on the median covariation matrix...
  • LIBRA

  • Referenced in 28 articles [sw10553]
  • Robust Analysis is developed at ROBUST@Leuven, the research group on robust statistics ... contains user-friendly implementations of several robust procedures. These methods are resistant to outliers ... estimation (MCD), regression (LTS, MCD-regression), Principal Component Analysis (RAPCA, ROBPCA), Principal Component Regression (RPCR ... depth quantiles. For comparison also several non-robust functions are included. Many graphical tools...
  • amap

  • Referenced in 4 articles [sw19769]
  • Package. Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions...
  • rpca

  • Referenced in 1 article [sw29201]
  • Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM ... prove that we can recover each component individually under some suitable assumptions. It is possible ... sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among ... package implements this decomposition algorithm resulting with Robust PCA approach...
  • TOMCAT

  • Referenced in 3 articles [sw01049]
  • user-friendly graphical interface for robust calibration with a collection of m-files, called TOMCAT ... implemented methods there are Principal Component Analysis and its robust variant, Partial Least Squares, Continuum...
  • ipPCA

  • Referenced in 2 articles [sw33855]
  • iterative pruning principal component analysis and structure. Conclusions: The EigenDev heuristic is robust to sampling...
  • ChemoSpec

  • Referenced in 1 article [sw15966]
  • cluster analysis (HCA), principal components analysis (PCA) and model-based clustering. Robust methods appropriate...
  • Rfwdmv

  • Referenced in 3 articles [sw25518]
  • which implements the forward search for the analysis of multivariate data. The package provides functions ... data and for testing in a robust way whether the data should be transformed. Additionally ... package contains functions for performing robust principal component analyses and robust discriminant analyses as well...
  • GWmodel

  • Referenced in 5 articles [sw08531]
  • summary statistics and a GW principal components analysis; (ii) advanced GW regression fits and diagnostics ... stationarity; (iv) a GW discriminant analysis; and (v) enhanced kernel bandwidth selection procedures. General Election ... GWmodel study, which focuses on basic and robust GW models...
  • gNCA

  • Referenced in 4 articles [sw08821]
  • classical approaches such as principal component analysis or independent component analysis, NCA makes ... enables a more accurate and self-consistent analysis over different experiments and extends ... significantly expands the capability of transcription network analysis by incorporating regulatory signal constraints arising from ... derived. In addition, numerical techniques for robust decomposition are discussed. gNCA is then demonstrated using...
  • sparsepca

  • Referenced in 1 article [sw31562]
  • Principal Component Analysis (SPCA). Sparse principal component analysis (SPCA) attempts to find sparse weight vectors ... principal components in high-dimensional data settings. This is, because the principal components are formed ... fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows...
  • RepExplore

  • Referenced in 1 article [sw30166]
  • robust averages may help to reduce the influence of noise on downstream data analysis ... programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing ... tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data...
  • RVMAB

  • Referenced in 1 article [sw24037]
  • Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce ... prediction model is efficiency and robust. It can be an automatic decision support tool...
  • ANSYS

  • Referenced in 666 articles [sw00044]
  • ANSYS offers a comprehensive software suite that spans...