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Some of the next articles are maybe not open access.
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
Female erectile tissues and sexual dysfunction after pelvic radiotherapy: A scoping review
Ca-A Cancer Journal for Clinicians, 2022Deborah C Marshall, Mas +2 more
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
Low-dimensional wide-bandgap semiconductors for UV photodetectors
Nature Reviews Materials, 2023Ziqing Li, Xiaosheng Fang
exaly
Clinical development and potential of photothermal and photodynamic therapies for cancer
Nature Reviews Clinical Oncology, 2020Xing-Shu Li +2 more
exaly
Impairment‐driven cancer rehabilitation: An essential component of quality care and survivorship
Ca-A Cancer Journal for Clinicians, 2013Julie K Silver
exaly