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Bookmark or Share- Step-By-Step Guide to Principal Component Analysis With Example - TuringPCA stands for Principal Component Analysis. It is one of the popular and unsupervised algorithms that has been used across several applications like data analysis, data compression, de-noising, reducing the dimension of data and a lot more.
- What Is Principal Component Analysis (PCA)? | IBMPrincipal component analysis (PCA) reduces the number of dimensions in large datasets to principal components that retain most of the original information. It does this by transforming potentially correlated variables into a smaller set of variables, called principal components.
- Principal Component Analysis Guide & Example - Statistics By JimPrincipal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the original set of variables. Analysts refer to these new values as principal components.
- Principal component analysis - WikipediaPrincipal component analysis ( PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing . The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
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