Knowledge Discovery by Accuracy Maximization
Maximization of Cross-Validateed Accuracy Methods
Ulisse Dini Data Set Generator
Find Shortest Paths Between All Nodes in a Graph
Helicoid Data Set Generator
Kabsch Algorithm
Internal Grid Functions
Knowledge Discovery by Accuracy Maximization
Visualization of KODAMA output
Evaluation of the Monte Carlo accuracy results
Default configuration for RMDS
Normalization Methods
Principal Components Analysis
Scaling Methods
Spirals Data Set Generator
Swiss Roll Data Set Generator
Conversion Classification Vector to Matrix
Default configuration for Rtsne
Default configuration for umap
A self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. It facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. The method incorporates a novel strategy to integrate spatial information, improving the interpretability of results in spatially resolved data.