Original video: https://youtu.be/a3nwq5HFuF8
Delivered on: 22 DECEMBER 2022
Principal component analysis (PCA) is a way to bring out strong patterns from large and complex datasets. The essence of the data is captured in a few principal components, which themselves convey the most variation in the dataset.
PCA reduces the number of dimensions without selecting or discarding them. Instead, it constructs principal components that focus on variation and account for the varied influences of dimensions. Such influences can be traced back from the PCA plot to find out what produces the differences among clusters.
Keywords: principal component analysis (PCA), dimensionality reduction, data visualization, multivariate analysis, eigenvalues, eigenvectors, correlation, covariance, biplot, score plot, loading plot, scree plot, data interpretation, statistical analysis
Location
Faculty of Agriculture, Universiti Putra Malaysia
Fakulti Pertanian, Universiti Putra Malaysia, 43000 Seri Kembangan, Selangor
XPMM+9J Seri Kembangan, Selangor
2.9845517506267742, 101.73803356324866
Attribution 4.0 International — CC BY 4.0 - Creative Commons
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