Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:Baronet
|
| gptkbp:application |
Bioinformatics
Data Visualization Image Processing Machine Learning Natural Language Processing |
| gptkbp:basedOn |
Manifold Learning
Topological_Data_Analysis |
| gptkbp:benefits |
Captures Global Structure
Preserves Local Structure Sensitive to Parameter Settings Not_Interpretable_as_PCA |
| gptkbp:community |
Researchers
Data_Scientists Machine_Learning_Practitioners |
| gptkbp:developedBy |
gptkb:John_Healy
gptkb:James_Melville Leland_McInnes |
| gptkbp:dimensions |
2D
3D |
| gptkbp:drawings |
gptkb:Matplotlib
Plotly Seaborn |
| gptkbp:firstPublished |
2018
Journal of Open Source Software |
| https://www.w3.org/2000/01/rdf-schema#label |
UMAP
|
| gptkbp:inputOutput |
High-dimensional_Data
|
| gptkbp:language |
gptkb:C++
Python R |
| gptkbp:library |
umap-learn
umap |
| gptkbp:notableEvent |
Probabilistic
Non-linear |
| gptkbp:performance |
Faster than t-SNE
Scalable to large datasets |
| gptkbp:powerOutput |
Low-dimensional Representation
|
| gptkbp:providesAccessTo |
Yes
|
| gptkbp:relatedTo |
Artificial Intelligence
Data Mining Statistics Feature Engineering t-SNE PCA Data_Science |
| gptkbp:setting |
metric
spread min_dist n_neighbors random_state |
| gptkbp:uses |
Stochastic Optimization
Nearest Neighbors Graph-based_Approach |