Robust PCA
A implementation of robust PCA, useful when the data contains outliers.
Category: Principal Components Analysis
Multi-class classification for NIR spectroscopy: the very basics
Multi-class classification aims and subdividing samples into one of multiple predefined categories. In this post we explore a basic classifier and discuss important metrics such as accuracy and AUC …
The PCA correlation circle
The PCA correlation circle is a useful tool to visually display the correlation between spectral bands and principal components. The correlation can be quantified through the Euclidean distance and …
PCA and kernel PCA explained
Principal Components Analysis
06/10/2020
Gain a practical understanding of PCA and kernel PCA by learning to code the algorithms and test it on real spectroscopic data.
Detecting outliers using the Mahalanobis distance with PCA in Python
How do we make sure we are detecting only true outliers and not cherry-picking from the data? Here's a method based on the Mahalanobis distance with PCA.
Classification of NIR spectra using Principal Component Analysis in Python
Classification, Principal Components Analysis
03/23/2018
An in-depth tutorial on how to run a classification of NIR spectra using Principal Component Analysis in Python. Step by step example with code.
NIR classification of macadamia kernels
Classification, Principal Components Analysis, Use Cases
07/06/2017
Can we use NIR analysis to grade macadamias? Check out our preliminary results of NIR classification of macadamia kernels using Principal Component Analysis.
The cricket thermometer: an introduction to Principal Component Analysis in Python
Classification, Principal Components Analysis
03/21/2017
A worked example for an introduction to Principal Component Analysis in Python.
