Results 311 to 320 of about 2,775,431 (371)

A multi-view representation technique based on principal component analysis for enhanced short text clustering. [PDF]

PLoS One
Ahmed MH, Tiun S, Omar N, Sani NS.
europepmc   +1 more source

Long-Term Effects of Neurofeedback and Hyperbaric Oxygen Therapy on Traumatic Brain Injury: A Principal Component Analysis (PCA)-Based Secondary Analysis. [PDF]

Cureus
Peterson T, AbouAssaly J, Burgin S, Sherwin R, Strale F.   +4 more
europepmc   +1 more source

Computational Analysis of the Micromechanical Stress Field in Undamaged and Damaged Unidirectional Fiber-Reinforced Plastics Using a Modified Principal Component Analysis. [PDF]

Polymers (Basel)
Lopez NR, Çelik H, Hopmann C.
europepmc   +1 more source

A novel framework for elucidating the effect of mechanical loading on the geometry of ovariectomized mouse tibiae using principal component analysis. [PDF]

Front Bioeng Biotechnol
Moraiti S, Cheong VS, Dall'Ara E, Kadirkamanathan V, Bhattacharya P.   +4 more
europepmc   +1 more source

Enhancing NILM classification via robust principal component analysis dimension reduction. [PDF]

Heliyon
Yaniv A, Beck Y.
europepmc   +1 more source

Derterminants of the farm income for the suckler cattle farms. Principal component analysis with a 64 charolais farms sample in north Massif Central

, 1999
Patrick Veysset, Patrick Gasqui, Michel Lherm, Didier Bébin   +3 more
openalex   +1 more source

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Principal component analysis

Nature Reviews Methods Primers, 2022
Principal component analysis is a versatile statistical method for reducing a cases-byvariables data table to its essential features, called principal components. Principal components are a few linear combinations of the original variables that maximally explain the variance of all the variables.
Michael Greenacre, Patrick J. F. Groenen, Trevor Hastie, Alfonso Iodice D’Enza, Angelos Markos, Elena Tuzhilina   +5 more
semanticscholar   +6 more sources

Kernel Principal Component Analysis

International Conference on Artificial Neural Networks, 1997
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Schölkopf, B., Smola, A., Müller, K.
openaire   +6 more sources