- The idea of the principal component analysis (PCA) or KL transform is to transform a given set of measurements to a new set of features so that the features exhibit high information packing properties.
- This leads to a reduced and compact set of features. Basically, this elimination is made possible because of the information redundancies. This compact representation is of a reduced dimension.
The PCA algorithm is as follows:
The new data is a dimensionally reduced matrix that represents the original data. Therefore, PCA is effective in removing the attributes that do not contribute. If the original data is required, it can be obtained with no loss of information.
