
PCASIFT
 Referenced in 78 articles
[sw04592]
 PCASIFT: 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 PCAbased local descriptors are more distinctive, more robust to image deformations...

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

Kernlab
 Referenced in 88 articles
[sw07926]
 includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver...

Eigentaste
 Referenced in 59 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...

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

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...

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

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

MultiPIE
 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 19 articles
[sw19668]
 fdasrvf. Elastic Functional Data Analysis. Performs alignment, PCA, and modeling of multidimensional and unidimensional functions...

elasticnet
 Referenced in 14 articles
[sw08181]
 ElasticNet 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...

pcaMethods
 Referenced in 6 articles
[sw19779]
 Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse NonLinear 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...

homals
 Referenced in 12 articles
[sw13953]
 category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal...

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

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...