Python Hyperspectral Analysis Tool (PyHAT) Outlier Identification Example
![Scatter plot showing PCA scores as points. Several points are marked in red as outliers.](https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/styles/full_width/public/media/images/outlier_example.png?itok=iX3CPHYQ)
Detailed Description
This figure shows an example of outlier identification 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. Dimensionality was then reduced using principal components analysis (PCA). Each point on the PCA scores plot corresponds to a spectrum. The isolation forest algorithm was used to identify potential outliers and these have been marked in red.
This figure is one of a series of figures used to demonstrate some of the capabilities of the PyHAT software.
Sources/Usage
Public Domain.