
PCASIFT
 Referenced in 78 articles
[sw04592]
 PCASIFT: 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 67 articles
[sw11592]
 method for robust principal component analysis (PCA). Classical PCA is based on the empirical covariance...

Eigentaste
 Referenced in 60 articles
[sw12451]
 items and applies Principal Component Analysis (PCA) to the resulting dense subset of the ratings...

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...

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 nonGaussian...

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...

ICtest
 Referenced in 3 articles
[sw36426]
 linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised...

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 (KDRPCR), KDR with partial least...

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

dyndimred
 Referenced in 2 articles
[sw33012]
 dimensionality reduction methods, such as Principal Component Analysis (’PCA’), Independent Component Analysis (’ICA’), diffusion maps...

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...