Statements (42)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
dimensionality reduction technique
manifold learning algorithm |
| gptkbp:abbreviation |
gptkb:LLE
|
| gptkbp:application |
gptkb:data_visualization
feature extraction preprocessing |
| gptkbp:assumes |
data lies on a low-dimensional manifold
|
| gptkbp:category |
unsupervised learning
manifold learning nonlinear dimensionality reduction |
| gptkbp:citation |
high
|
| gptkbp:field |
gptkb:machine_learning
data science statistics |
| https://www.w3.org/2000/01/rdf-schema#label |
Locally Linear Embedding
|
| gptkbp:influenced |
gptkb:Laplacian_Eigenmaps
Hessian LLE Modified LLE |
| gptkbp:input |
high-dimensional data
|
| gptkbp:introduced |
Lawrence K. Saul
Sam T. Roweis |
| gptkbp:introducedIn |
2000
|
| gptkbp:limitation |
computationally expensive for large datasets
does not preserve global structure sensitive to noise |
| gptkbp:openSource |
gptkb:MATLAB
gptkb:scikit-learn R |
| gptkbp:output |
low-dimensional embedding
|
| gptkbp:publishedIn |
gptkb:science
|
| gptkbp:relatedTo |
gptkb:Isomap
gptkb:PCA gptkb:t-SNE nonlinear dimensionality reduction |
| gptkbp:step |
compute low-dimensional embedding
compute reconstruction weights find nearest neighbors |
| gptkbp:supportsAlgorithm |
unsupervised learning
|
| gptkbp:url |
https://scikit-learn.org/stable/modules/manifold.html#locally-linear-embedding
|
| gptkbp:bfsParent |
gptkb:Isomap
gptkb:Sam_Roweis |
| gptkbp:bfsLayer |
7
|