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About 10,000 papers for “Principal component analysis”

Principal component analysis

Ana Palac · University of Split Repository · 2022

U ovome radu obrađena je metoda redukcije dimenzionalnosti analiza glavnih komponenti pretprocesiranja podataka u strojnom učenju. U početku je prikazana potreba za pretprocesiranjem podataka i četiri važna koraka kroz koje podaci moraju proći da bi bili uspješno obrađeni. Zatim je šire opisana metoda redukcije dimenzionalnosti, njezin značaj, prednosti i metode. Za kraj je objašnjena metoda analize glavnih komponenti gdje je prikazana matematička pozadina metode i implementacija.

Cited by 0

Principal component analysis.

Katelyn Del Toro (9211907), Rosalyn Sayaman (17883737), Kate Thi (15140569) et al. · Figshare · 2024

<p><b>(a)</b> Projection of breast cell types on principal components 1 and 2 (PC1, PC2). PC1 separates the luminal ER<sup>Pos</sup> and ER<sup>Neg</sup>, Pop4 epithelial, and myoepithelial cell types (epithelial lineages) from the mesenchymal (fibroblasts, adipocytes, pericytes, and vSMCs) and endothelial (lymphatic and vascular) lineages. PC2 separates the CD45<sup>Pos</sup> leukocytes (mesodermal lineage) from all other cell types. The cell types composing each of the epithelial, mesenchymal, endothelial, and mesodermal cell lineages are indicated (dashed circles). Data is available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3002820#pbio.3002820.s004" target="_blank">S3 Data</a>. <b>(b)</b> Median transcript levels of the 50 loading factors (genes) that contribute to the negative end of PC1 (bottom end of the list, rlog/log2 scale; Supplementary Data File 13 at 10.17605/OSF.IO/GFHC3). <b>(c)</b> Median mRNA expression of the 50 loading factors (genes) contributing to the negative end of PC2 (bottom end of the list) that separates the leukocyte samples (rlog, log2 scale; Supplementary Data File 13 at 10.17605/OSF.IO/GFHC3). Stars (★) denote commonly used markers for luminal cells (blue), myoepithelial cells (red), and leukocytes (pink). PC, principal component.</p>

Cited by 0Open access

Principal component analysis.

Daniel Belstrøm (847539), Palle Holmstrup (700174), Allan Bardow (847540) et al. · Figshare · 2016

<p>Principal component analysis visualized 2-dimensionally with axes expressed as the second and third most crucial components accounting for 36.6% of the variation of the dataset. Individual 1: blue square, individual 2: red square, individual 3: green triangle, individual 4: purple cross, individual 5: light blue cross.</p>

Cited by 0Open access

Principal component analysis.

Carla A. Piccinato (459904), Guilherme J. M. Rosa (230655), Alhaji U. N’Jai (459905) et al. · Figshare · 2015

<p>Principal component analysis (PCA) using gene expression measurements from 592 genes selected based on significance level (<i>P</i><0.01). Control samples (CO) clustered effectively separated from the estradiol-17β (E2), progesterone (P4) and estradiol-17β+progesterone (E2P4T) treated samples.</p>

Cited by 0Open access

Principal component analysis.

Ana Belén Galán López (16806749), Maxime Pelletier (16806752), Emmanuel Discamps (837162) · Figshare · 2023

<p>Scatter plots of the two first axes (PC1 and PC2) of principal component analyses performed on the shape data for <i>Rangifer tarandus caribou</i> according to habitat (mountain, tundra and boreal forest) and mobility type (sedentary, planarly and altitudinal); (a) Forelimb; (b) hindlimb.</p>

Cited by 0Open access

Principal Component Analysis.

Cidália Gomes (3155643), Ronaldo Sousa (3155631), Tito Mendes (3155634) et al. · Figshare · 2016

<p>The PCA showing the relationship of the PC1 and PC2 components of <i>Corbicula fluminea</i> populations from rivers of the North, Centre and South of Portugal, and the Lima River (North Portugal). Each circle represents one specimen corresponding to a specific river. The N cluster in the light red oval circle represents <i>C</i>. <i>fluminea</i> populations from the northern rivers and C/S in light green oval represents the centre and southern rivers cluster.</p>

Cited by 0Open access

Principal component analysis.

Pia Leandersson (4859443), Anna Åkesson (2046415), Ingrid Hedenfalk (75489) et al. · Figshare · 2020

Cited by 0Open access

Principal component analysis.

Elke S. Reichwaldt (424042), Haihong Song (424043), Anas Ghadouani (424044) · Figshare · 2015

<p>Principal component analysis (PCA) of 15 main parameters that describe the sampling sites. Eigenvectors of the first two principal components (PC), that explain a total of 63.3% of the variation, are given. Bold numbers depict major contrasts. DM = dry mass. The bottom line shows the percentage of variation explained by the respective PC.</p>

Cited by 0Open access

Principal component analysis.

Giordano Mancini (114178), Costantino Zazza (801006) · Figshare · 2015

<p>Projection of the trajectories of Cα atoms from the larger artificial trajectory in the volume spanned by the first 3 eigenvectors obtained by the principal component analysis, from 2 different points of view. Frames have been represented as dots coloured in black (WT), blue (F429A), magenta (F429E), orange (F429H) and green (F429L).</p>

Cited by 0Open access

Principal component analysis.

Axel Schmidt (652713), Nicolas Casadei (5132210), Fabian Brand (14052977) et al. · Figshare · 2024

<p>For each individual, principal components were calculated based on the “common variants for QC” variant set. (A) The first two principal components (PC1 and PC2) are plotted for all individuals of DeCOI (empty forms) together with individuals from the 1000 genomes project (1 KG reference cohort, grey circles). Individuals assigned to the European subcohort of DeCOI (DeCOI<sub>EUR</sub>) are plotted in blue circles, while all others are indicated in black triangles. The region marked by the dashed box is enlarged in panels B-D. (B) and (C): The individuals of DeCOI<sub>EUR</sub> are plotted within the PC-space, colored by their case-control definitions in analyses Ex and B1. In (D), all individuals of DeCOI<sub>EUR</sub> are plotted with colors indicating their respective site of sequencing.</p> <p>(JPG)</p>

Cited by 0Open access

Principal component analysis.

Catherine Mooney (76525), Rana Raoof (839747), Hany El-Naggar (839748) et al. · Figshare · 2016

<p>AM-PM—Samples taken from 5 females and 5 males, one morning sample and one afternoon sample from each is used (Plot created with R package ggbiplot).</p>

Cited by 0Open access

Principal Component Analysis

? · 2007

Introduction The Karhunen-Lo`eve basis functions, more frequently referred to as principal components or empirical orthogonal functions (EOFs), of the noise response of the climate system are an important tool for geophysical studies. Many researchers have used this tool to examine the geophysical and climatological phenomena. Perhaps more frequent use of EOFs in recent studies is in conjunction with the development of the signal detection and estimation methods (e.g., Barnett et al. 1991; Hasselmann 1993; Hegerl et al. 1994; Santer et al. 1994; Shen et al. 1994; North et al. 1995; North and Kim 1995; Kim et al. 1996c). In such studies, the frequencydependent EOFs (fdEOFs; Wallace and Dickinson 1972; North 1984; Hasselmann 1988; Kim and North 1993) of the background fluctuations of a detection variable serve as an orthogonal basis set and are used to design optimal techniques for detecting and estimating signals. A detection and prediction approach is to design a filter or opti

Cited by 0

Principal Component Analysis.

Ananya Muddukrishna (5662918), Peter A. Jonsson (720583), Mats Brorsson (720584) · Figshare · 2015

<p>(a) Variation explained by Principal Components. We retain the first four Principal Components which explain more than 85% variation. (b) Loadings of retained Principal Components.</p>

Cited by 0Open access

Principal Component Analysis

National Cancer Institute · Definitions · 2020

A vector space transform used to reduce the dimensionality in a dataset while retaining those characteristics of the dataset that contribute most to its variance.

Cited by 6Open access