Results 231 to 240 of about 2,306,590 (284)

A comprehensive evaluation of drought resistance in Hemerocallis fulva L. using membership function and principal component analysis. [PDF]

Sci Rep
Liang Z, Pei K, Zhang H, Lai X, Meng Y, Jia M, Cao D, Zhang C, Song Z, Duan J.   +9 more
europepmc   +1 more source

Robust sparse smooth principal component analysis for face reconstruction and recognition. [PDF]

PLoS One
Wang J, Xie X, Zhang L, Li J, Cai H, Feng Y.   +5 more
europepmc   +1 more source

Kernel principal component analysis-based water quality index modelling for coastal aquifers in Saudi Arabia. [PDF]

Sci Rep
Aldrees A, Jibrin AM, Dan'azumi S, Al-Suwaiyan M, Abba SI, Yaseen ZM.   +5 more
europepmc   +1 more source

Multi-joint isokinetic strength profiling as a predictor of vertical jump performance in elite freestyle wrestlers: A cross-sectional principal component analysis. [PDF]

Medicine (Baltimore)
Sever O, Ceylan Hİ, Dhahbi W, Yilmaz HH, Özdemir K, Yazici AG, Jalalov F, Günay AR, Bayrakdaroğlu S, Stefanica V.   +9 more
europepmc   +1 more source

Evaluating the Output Performance of the Semiconductor Bridge Through Principal Component Analysis. [PDF]

Nanomaterials (Basel)
Zhang L, Da Y, Zhang W, Li F, Xu J, Jing L, Liu Q, Ye Y, Shen R.   +8 more
europepmc   +1 more source

Cognitive modes involved in emotion regulation identified using Constrained Principal Component Analysis for fMRI (fMRI-CPCA)

van Reekum CM, Tupitsa E, Lloyd WK, Momeni A, Shahki J, Feredoes E, Woodward TS.   +6 more
europepmc   +1 more source

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Coupled Principal Component Analysis

IEEE Transactions on Neural Networks, 2004
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
openaire   +5 more sources

Principal Components Analysis

2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D., Hartmann, S., Klie, S., Selbig, J.   +3 more
openaire   +3 more sources