A framework for group-based screening of large parameter spaces.

UMAP testing

Mutual Information-based Non-linear Clustering Analysis

Matrix decomposition algorithms including PCA (principal component analysis) and ICA (independent component analysis)

TorchDR - PyTorch Dimensionality Reduction

Self-Supervised Noise Embeddings (Self-SNE)

This repository contains some supplementary materials (code, data and plots) for the paper “Resilience Potential of the Russian Arctic Cities".

A Snakemake workflow to split, filter, normalize, integrate and select highly variable features of count matrices resulting from experiments with sequencing readout (e.g., RNA-seq, ATAC-seq, ChIP-seq, Methyl-seq, miRNA-seq,...) including diagnostic visualizations.

An R package implementing the UMAP dimensionality reduction method.

Short text clustering methods through differents approaches

Comparative analysis of different feature extraction techniques for hyperspectral image classification.

Uniform Manifold Approximation and Projection

This project involves the application of dimensionality reduction techniques i.e., Principal Component Analysis (PCA), on a cancer patients dataset. The goal is to simplify the dataset by reducing its dimensionality, making it easier to visualize and analyze, while retaining essential information.

Functions that filter UMAP graphs using domain knowledge.

Single cell trajectory detection

Compressed belief-state MDPs in Julia compatible with POMDPs.jl

Non-linear dimensionality reduction using contrastive neighbor embeddings.

A Toolbox for Dynamic Mapping in Python

SLISEMAP: Combining supervised dimensionality reduction with local explanations