• PCA-SIFT

  • Referenced in 78 articles [sw04592]
  • PCA-SIFT: A More Distinctive Representation for Local Image Descriptors Stable local feature detection ... weighted histograms, we apply Principal Components Analysis (PCA) to the normalized gradient patch. Our experiments ... demonstrate that the PCA-based local descriptors are more distinctive, more robust to image deformations...
  • ROBPCA

  • Referenced in 67 articles [sw11592]
  • method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance...
  • Kernlab

  • Referenced in 91 articles [sw07926]
  • includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver...
  • Eigentaste

  • Referenced in 60 articles [sw12451]
  • items and applies Principal Component Analysis (PCA) to the resulting dense subset of the ratings ... matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations...
  • LowRankModels

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

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

  • Referenced in 35 articles [sw04804]
  • arising in the direct sparse PCA method...
  • SVM

  • Referenced in 30 articles [sw14904]
  • Wavelet Kernel; SVM Based Feature Selection; Kernel PCA; Kernel Discriminant Analysis; SVM Based Feature selection...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • dual formulations have been given to kernel PCA, kernel CCA and kernel PLS. Recent developments...
  • Multi-PIE

  • Referenced in 25 articles [sw31361]
  • furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities...
  • missMDA

  • Referenced in 15 articles [sw08142]
  • imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model ... model; Perform multiple imputation with and in PCA...
  • fdasrvf

  • Referenced in 20 articles [sw19668]
  • fdasrvf. Elastic Functional Data Analysis. Performs alignment, PCA, and modeling of multidimensional and unidimensional functions...
  • elasticnet

  • Referenced in 14 articles [sw08181]
  • Elastic-Net for Sparse Estimation and Sparse PCA. This package provides functions for fitting...
  • pcaPP

  • Referenced in 13 articles [sw11596]
  • pcaPP: Robust PCA by Projection Pursuit. R package...
  • homals

  • Referenced in 13 articles [sw13953]
  • category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal...
  • Flavia

  • Referenced in 13 articles [sw22234]
  • stepwise method), all these characters are reserved. PCA orthogonalizes these 12 characters into 5 principal...
  • pcaMethods

  • Referenced in 6 articles [sw19779]
  • Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional ... PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA ... NipalsPCA may be used to perform PCA on incomplete data as well as for accurate ... plotting the results is also provided. All PCA methods make use of the same data...
  • Isomap

  • Referenced in 11 articles [sw31686]
  • classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach...
  • rsvd

  • Referenced in 7 articles [sw16104]
  • used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized ... PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components...
  • GSPPCA

  • Referenced in 4 articles [sw25977]
  • Bayesian variable selection for globally sparse probabilistic PCA. Sparse versions of principal component analysis ... PCA) have imposed themselves as simple, yet powerful ways of selecting relevant features of high ... this end, using Roweis’ probabilistic interpretation of PCA and an isotropic Gaussian prior ... marginal likelihood of a Bayesian PCA model. Moreover, in order to avoid the drawbacks...