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Principal components analysis corrects for stratification in genome-wide association studies
Nature Genetics, 2006A. Price +5 more
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Journal of Personality and Social Psychology, 1988
R. Raskin, H. Terry
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R. Raskin, H. Terry
semanticscholar +1 more source
2012
Among linear DR methods, principal component analysis (PCA) perhaps is the most important one. In linear DR, the dissimilarity of two points in a data set is defined by the Euclidean distance between them, and correspondingly, the similarity is described by their inner product.
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Among linear DR methods, principal component analysis (PCA) perhaps is the most important one. In linear DR, the dissimilarity of two points in a data set is defined by the Euclidean distance between them, and correspondingly, the similarity is described by their inner product.
openaire +2 more sources
Principal components analysis and track quality index: A machine learning approach
Transportation Research Part C: Emerging Technologies, 2018A. Lasisi, N. Attoh-Okine
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Social determinants of health and US cancer screening interventions: A systematic review
Ca-A Cancer Journal for Clinicians, 2023Ariella R Korn
exaly
2008
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns in multivariate data. It aims to graphically display the relative positions of data points in fewer dimensions while retaining as much information as possible, and explore relationships between dependent variables. It is a hypothesis-generating technique
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Principal components analysis (PCA) is a multivariate ordination technique used to display patterns in multivariate data. It aims to graphically display the relative positions of data points in fewer dimensions while retaining as much information as possible, and explore relationships between dependent variables. It is a hypothesis-generating technique
openaire +2 more sources