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Concept
Principal Component Analysis (PCA)
Principal Component Analysis
(PCA) is a
dimensionality reduction technique
that transforms a
large set of variables
into a
smaller one
that still contains most of the
information in the original dataset
. It achieves this by identifying the directions, called
principal components
, along which the
variation in the data
is maximized, allowing for
easier visualization
and analysis while
mitigating noise
and redundancy.
Relevant Fields:
Probability and Statistics 70%
Computer Science and Data Processing 20%
Computational Mathematics 10%
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