Open Lab
Explore chemometrics and analytical science through theory, methods, code, and practice.
26 resources
26 resources
HCA
Hierarchical Cluster Analysis
Unsupervised agglomerative clustering that starts from singleton observations and merges clusters under a chosen distance metric and linkage criterion, producing a nested hierarchy.
k-Means
k-Means Clustering
Unsupervised partitioning that seeks K centroids minimizing within-cluster squared Euclidean error by alternating nearest-centroid assignment and mean updates.
k-NN
k-Nearest Neighbors
Supervised classification based on the labels of the nearest training observations under a defined distance metric.
LDA
Linear Discriminant Analysis
Supervised classification using class means, a shared covariance structure, and prior class probabilities.
MCR-ALS
Multivariate Curve Resolution - Alternating Least Squares
Bilinear mixture resolution that alternates least-squares updates of contribution and response profiles under selected chemical constraints.
RF
Random Forest
Supervised classification by an ensemble of randomized trees. Classical Random Forests bootstrap the training rows, consider a random subset of predictors at each split, and aggregate tree outputs.
SIMCA
Soft Independent Modelling of Class Analogy
Class modelling with independent PCA models and a residual F acceptance rule, allowing one, several, or no class memberships.
SVM
Support Vector Machines
Maximum-margin binary classification with a soft-margin penalty C, dual support-vector expansion, and optional kernels. Multiclass SVM is built from binary classifiers.
Baseline Correction
A baseline estimation approach that combines smoothness penalization with asymmetric weighting to separate a slowly varying background from spectral peaks.