• PCA-SIFT

  • Referenced in 78 articles [sw04592]
  • PCA-SIFT: A More Distinctive Representation for Local Image Descriptors Stable local feature detection ... representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk ... smoothed weighted histograms, we apply Principal Components Analysis (PCA) to the normalized gradient patch...
  • ROBPCA

  • Referenced in 66 articles [sw11592]
  • method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance...
  • Eigentaste

  • Referenced in 59 articles [sw12451]
  • items and applies Principal Component Analysis (PCA) to the resulting dense subset of the ratings...
  • LowRankModels

  • Referenced in 24 articles [sw27002]
  • data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative matrix factorization...
  • Pyglrm

  • Referenced in 23 articles [sw27003]
  • data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative matrix factorization...
  • missMDA

  • Referenced in 15 articles [sw08142]
  • values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model...
  • Isomap

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

  • Referenced in 7 articles [sw16104]
  • widely 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 ... function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions...
  • KFDB

  • Referenced in 6 articles [sw03397]
  • face recognition performed using PCA(Principal Component Analysis), LFA (Local Feature Analysis) and correlation matching...
  • GSPPCA

  • Referenced in 4 articles [sw25977]
  • probabilistic PCA. Sparse versions of principal component analysis (PCA) have imposed themselves as simple ... unsupervised manner. However, when several sparse principal components are computed, the interpretation of the selected...
  • ePCA

  • Referenced in 4 articles [sw27830]
  • ePCA: high dimensional exponential family PCA. Many applications involve large datasets with entries from exponential ... molecular structure. In XFEL, estimating the principal components of the noiseless distribution is needed ... determination. However, the standard method, Principal Component Analysis (PCA), can be inefficient in non-Gaussian...
  • factoextra

  • Referenced in 3 articles [sw18447]
  • multivariate data analyses, including ’PCA’ (Principal Component Analysis), ’CA’ (Correspondence Analysis), ’MCA’ (Multiple Correspondence Analysis...
  • onlinePCA

  • Referenced in 3 articles [sw21315]
  • routinely perform tasks like principal component analysis (PCA). Recursive algorithms that update the PCA with...
  • rCUR

  • Referenced in 3 articles [sw16750]
  • reduct their dimensions. Principal component analysis (PCA) is a widely used tool for such data...
  • MDI

  • Referenced in 2 articles [sw35028]
  • among variables. In this way, principal component analysis (PCA) models are fitted iteratively to impute ... missing data until convergence. Different methods, using PCA internally, are included in the toolbox: trimmed ... known data regression (KDR), KDR with principal component regression (KDR-PCR), KDR with partial least...
  • MPCI

  • Referenced in 2 articles [sw08836]
  • followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis...
  • PRINQUAL

  • Referenced in 1 article [sw14474]
  • Software: The PRINQUAL procedure performs principal component analysis (PCA) of qualitative, quantitative, or mixed data...
  • pcamv

  • Referenced in 1 article [sw24055]
  • Matlab package for PCA for datasets with missing values: The Matlab toolbox contains ... variants of probabilistic models for principal component analysis (PCA) in the presence of missing values...
  • freqdom

  • Referenced in 2 articles [sw32409]
  • Domain Based Analysis: Dynamic PCA. Implementation of dynamic principal component analysis (DPCA), simulation...
  • ICtest

  • Referenced in 1 article [sw36426]
  • linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised...