Uniform Manifold Approximation and Projection

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique used to visualize and analyze high-dimensional data by preserving the underlying data structure in a lower-dimensional space. Current research focuses on improving UMAP's speed and accuracy, including developing approximate versions for real-time applications and integrating it with other methods like correlated clustering and projection for enhanced performance in analyzing diverse data types such as biomedical images, brain signals, and network traffic. This technique's ability to reveal hidden patterns and outliers in complex datasets has significant implications for various fields, including healthcare, neuroscience, and cybersecurity, facilitating improved data analysis and anomaly detection.

Papers

July 31, 2024

Outlier Detection in Large Radiological Datasets using UMAP
Mohammad Tariqul Islam, Jason W. Fleischer
MAESTRO Dataset High Quality Outlier Detection Manifold Approximation Uniform Manifold Approximation and Projection

April 5, 2024

Approximate UMAP allows for high-rate online visualization of high-dimensional data streams
Peter Wassenaar, Pierre Guetschel, Michael Tangermann
Visualization Tool Multidimensional Projection Self Reflection Uniform Manifold Approximation and Projection

February 8, 2024

A Survey on Safe Multi-Modal Learning System
Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng
Timely Survey Native Robustness Multi Modal Learning Product Safety Idiom Uniform Manifold Approximation and Projection

December 8, 2023

Critical Analysis of 5G Networks Traffic Intrusion using PCA, t-SNE and UMAP Visualization and Classifying Attacks
Humera Ghani, Shahram Salekzamankhani, Bal Virdee
Nearest Neighbor Principal Component Analysis Data Dimensionality G Network SNE Algorithm Attack Classification Uniform Manifold Approximation and Projection

November 22, 2023

Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs
Soham Bafana
Long Short Term Memory Dimensionality Reduction Brain Computer Interface Visual Imagery Motor Imagery Classification Motor Impairment Functional Outcome Uniform Manifold Approximation and Projection

June 23, 2023

Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE
Yuta Hozumi, Gu-Wei Wei
Single Cell RNA Correlation Clustering scRNA Seq scRNA Seq Data Uniform Manifold Approximation and Projection

June 14, 2021

HUMAP: Hierarchical Uniform Manifold Approximation and Projection
Wilson E. Marcílio-Jr, Danilo M. Eler, Fernando V. Paulovich, Rafael M. Martins
Dimensionality Reduction Projection Bias Data Visualization High Dimensional Space Uniform Manifold Approximation and Projection