Principal Component Analysis

We apply the Principal Component Analysis on multi-dimensional data to reduce the dimensions while retaining maximal information by determining which features have maximum variance.

The examples provided here use triaxial raw data collected from an accelerometer and smoothed:

CODE

PCA p1 = new PCA(raw_signal, 1); // raw_signal of type double, output will be of 1 dimension
p1.fit();
double[][] output = p1.transform();

PCA OUTPUT