PCA is a multivariate data analysis technique to convert a set of observations into a set of values of linearly uncorrelated variables called principal components. Each principal component is a linear combination of the original variables.
The number of principal components is less than or equal to the number of original variables. Often, it is possible to retain most of the variability in the original variables with a very much smaller number of principal components. The analysis ensures that the first principal component accounts for as much of the variability in the data as possible and each succeeding component in turn has the next highest variance possible under the constraint that it is uncorrelated with the preceding components.
The objective of PCA is to:
- Discover patterns in experimental datasets.
- Reduce the dimensionality of the dataset.
- Identify new meaningful underlying variables.