An intuitive visualization to understand mathamatical heavy dimension reduction algorithms
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Updated
Jun 9, 2024 - Jupyter Notebook
An intuitive visualization to understand mathamatical heavy dimension reduction algorithms
Clustering images of skin diseases using DINOv2 embeddings and dimensionality reduction techniques.
My personal implementation of several unsupervised learning algorithms
Self-Supervised Noise Embeddings (Self-SNE)
Exploring Cybersecurity Data Science: Dimensionality Reduction and Cluster Analysis
an interactive explorer for flow cytometry data
Manifold Learning via Diffusion Maps in Julia
TorchDR - PyTorch Dimensionality Reduction
Easy genetic ancestry predictions in Python
FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
An R package for detecting cell-to-cell variably methylated regions (VMRs) from single-cell bisulfite sequencing.
This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.
Project to demonstrate various clustering algorithms for customer segmentation.
Increase processing efficiency via principal components analysis
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
This repository contains a project for customer segmentation using the K-Means clustering algorithm. The goal is to group customers into distinct clusters based on their demographic and purchasing behavior to better understand customer segments and target them effectively.
JavaScript implementation of UMAP
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