Results 221 to 230 of about 536,911 (259)
Some of the next articles are maybe not open access.
2011
One of the problems with a lot of sets of multivariate data is that there are simply too many variables to make the application of the graphical techniques described in the previous chapters successful in providing an informative initial assessment of the data. And having too many variables can also cause problems for other multivariate techniques that
Brian Everitt, Torsten Hothorn
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One of the problems with a lot of sets of multivariate data is that there are simply too many variables to make the application of the graphical techniques described in the previous chapters successful in providing an informative initial assessment of the data. And having too many variables can also cause problems for other multivariate techniques that
Brian Everitt, Torsten Hothorn
openaire +2 more sources
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.
openaire +2 more sources
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
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
openaire +2 more sources
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
The Affordable Care Act and access to care across the cancer control continuum: A review at 10 years
Ca-A Cancer Journal for Clinicians, 2020Jingxuan Zhao +2 more
exaly
Antibiotic resistance in the environment
Nature Reviews Microbiology, 2021D G Joakim Larsson, Carl-Fredrik Flach
exaly
Practical clinical interventions for diet, physical activity, and weight control in cancer survivors
Ca-A Cancer Journal for Clinicians, 2015Wendy Demark-Wahnefried +2 more
exaly