Principal Component
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Principal component analysis (PCA) is a mathematical procedure that uses an Orthogonal matrix|orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of Correlation and dependence|linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (i.e., uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is multivariate normal distribution#Joint_normality|jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field...
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