Python Hyperspectral Analysis Tool (PyHAT) Principal Component Analysis K-Means Clustering Example

This figure shows an example PCA plot generated using PyHAT. The input data were laser induced breakdown spectroscopy (LIBS) spectra. PyHAT was used to apply a baseline correction and normalization to the total intensity for each spectrum. The loading vectors for the first two pricipal components are shown at right, and the corresponding scores plot is shown at left. The points in the scores plot are colored based on clusters defined using the K-means algorithm with 8 clusters. 

This figure is one of a series of figures used to demonstrate some of the capabilities of the PyHAT software.