principal component analysis (PCA) is an unsupervised chemometric method that simultaneously analyzes all multidimensional information. this is a way to condense the data set, increase interpretability and preserve statistical information as much as decrease data loss. For low dimensionality data, PCA provides an easy manner to describe the variation in a sample set, for instance, how the samples resemble each other. The high-dimensional data is represented in a new lower-dimensional subspace that's spanned via the principal components of the largest variance within the original variables. PCA has been carried out in lots of disciplines and has been identified as a beneficial tool for interpreting data generated through color sensors for evaluation and analysis.
principal
component analysis (PCA) is an unsupervised
chemometric
method that
simultaneously
analyzes all multidimensional information.
this
is a way to condense the
data
set, increase interpretability and preserve statistical information as much as decrease
data
loss. For low dimensionality
data
, PCA provides an easy manner to
describe
the variation in a sample set,
for instance
, how the samples resemble each other. The high-dimensional
data
is represented
in a new lower-dimensional subspace that's spanned via the principal components of the largest variance within the original variables. PCA has
been carried
out in lots of disciplines and has
been identified
as a beneficial tool for interpreting
data
generated through color sensors for evaluation and analysis.
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