Results 101 to 110 of about 29,752 (189)

Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification. [PDF]

J Chem Theory Comput
Mollaei P, Barati Farimani A.
europepmc   +1 more source

Palmprint Recognition by Applying Wavelet Subband Representation and Kernel PCA

, 2008
. This paper presents a novel Daubechies-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition.
Murat Ekinci, Murat Aykut
core  

KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution. [PDF]

Front Genet
He Y, Jiang J, Qiu H, Shi YZ, Zhang BG.
europepmc   +1 more source

A novel and fast approach for population structure inference using kernel-PCA and optimization. [PDF]

Genetics, 2014
Popescu AA, Harper AL, Trick M, Bancroft I, Huber KT.   +4 more
europepmc   +1 more source

Interactive Knowledge-Based Kernel PCA for Solvent Selection. [PDF]

ACS Sustain Chem Eng
Boobier S, Heeley J, Gärtner T, Hirst JD.   +3 more
europepmc   +1 more source

Statistical Shape Analysis using Kernel PCA

, 2006
Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional feature space F.
Allen Tannenbaum, Yogesh Rathi Samuel, Samuel Dambreville   +2 more
core  

FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation. [PDF]

Sci Rep
Khan AR, Ahamad SS, Mishra S, Khan MAR, Sharma SK, AlEnizi A, Alfarraj O, Alowaidi M, Kumar M.   +8 more
europepmc   +1 more source

Kernel pca and de-noising in feature spaces

, 1999
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis.
Gunnar Rätsch, Smola, A., Scholz, M., Schölkopf, B., Bernhard Schölkopf, Sebastian Mika, Rätsch, G., Klaus-robert Müller, Mika, S., Müller, K., Alex Smola, Matthias Scholz   +11 more
core  

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems. [PDF]

BMC Bioinformatics, 2014
Thomas M, De Brabanter K, De Moor B.
europepmc   +1 more source

Kernel PCA per a l’anàlisi de dades òmiques

Les funcions kernel, per les seves propietats, permeten combinar diferents tipus de dades, essent una possible via per a la integració de dades òmiques.
Planell Picola, Núria
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