UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Explore this paper's citation graph
Summary
The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance.
- Type
- preprint
- Published
- 2018-02-09
- Cited by
- 13,196
- References
- 64
- Access
- Open access
- OpenAlex
- https://openalex.org/W2786672974
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3641284
Keywords
Projection (relational algebra), Dimensionality reduction, Dimension (graph theory), Reduction (mathematics), Mathematics
References
- Columbia Object Image Library (COIL100)
- Simplicial Homotopy Theory
- Classifying Clustering Schemes
- Accelerating t-SNE using tree-based algorithms
- A Survey on Metric Learning for Feature Vectors and Structured Data
- FlowRepository: A resource of annotated flow cytometry datasets associated with peer‐reviewed publications
- A global geometric framework for nonlinear dimensionality reduction.
- FUZZY SET THEORY AND TOPOS THEORY
- Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation
- Analysis of a complex of statistical variables into principal components.
- Survey Article: An elementary illustrated introduction to simplicial sets
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Efficient k-nearest neighbor graph construction for generic similarity measures
- Categories for the Working Mathematician
- Simplicial objects in algebraic topology
- Shift-invariant similarities circumvent distance concentration in stochastic neighbor embedding and variants
- A Nonlinear Mapping for Data Structure Analysis
- Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
- Graph Laplacians and their Convergence on Random Neighborhood Graphs
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
Cited by
- Organoid-induced differentiation of conventional T cells from human pluripotent stem cells
- Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments
- Quality control and evaluation of plant epigenomics data
- Unsupervised Feature Representation of Sleep EEG Data with Transient Deep Boltzmann Machine *
- Identifying Novel Subtypes of Functional Gastrointestinal Disorder by Analyzing Nonlinear Structure in Integrative Biopsychosocial Questionnaire Data
- Algorithms and Models for the Web Graph: 17th International Workshop, WAW 2020, Warsaw, Poland, September 21–22, 2020, Proceedings
- Complex systems: Features, similarity and connectivity
- May the force be with you
- CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets
- Efficient manifold approximation with spherelets
- Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
- Clusterdv, a simple density-based clustering method that is robust, general and automatic
- The Flatland Fallacy: Moving Beyond Low–Dimensional Thinking
- A high-bias, low-variance introduction to Machine Learning for physicists
- A more globally accurate dimensionality reduction method using triplets
- Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding
- Principal Component Analysis
- Evaluation of UMAP as an alternative to t-SNE for single-cell data
- Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
- Why Topology for Machine Learning and Knowledge Extraction?
Related papers
- Manifold learning and manifold alignment based on coupled linear projections
- A Fast Manifold Learning Algorithm for Dimensionality Reduction
- Framework of Multiple-point Statistical Simulation Using Manifold Learning for the Dimensionality Reduction of Patterns
- A General Framework for Manifold Alignment
- Overview of nonlinear dimensionality reduction methods in manifold learning
- A Novel Semi-Supervised Dimensionality Reduction Framework
- Multi-manifold LLE learning in pattern recognition
- Problems and Analysis in Manifold Learning
- Manifold Learning and its Application on Head Pose Estimation