Statements (41)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
dimensionality reduction technique
manifold learning algorithm |
| gptkbp:assumes |
data lies on a low-dimensional manifold
|
| gptkbp:basedOn |
gptkb:graph_Laplacian
|
| gptkbp:citation |
high
|
| gptkbp:field |
gptkb:machine_learning
computer vision data science pattern recognition manifold learning |
| https://www.w3.org/2000/01/rdf-schema#label |
Laplacian eigenmaps
|
| gptkbp:input |
similarity graph
|
| gptkbp:introduced |
gptkb:Partha_Niyogi
Mikhail Belkin |
| gptkbp:introducedIn |
2001
|
| gptkbp:output |
low-dimensional embedding
|
| gptkbp:publishedIn |
Neural Information Processing Systems (NIPS) 2001
|
| gptkbp:reduces |
graph-based cost function
|
| gptkbp:relatedTo |
gptkb:spectral_graph_theory
gptkb:Isomap gptkb:Locally_Linear_Embedding dimensionality reduction eigenvalue problem semi-supervised learning unsupervised learning Laplacian matrix spectral clustering diffusion maps graph embedding manifold hypothesis dimensionality reduction algorithms eigenmaps nonlinear embedding |
| gptkbp:step |
compute eigenvectors
compute graph Laplacian construct adjacency graph use eigenvectors for embedding |
| gptkbp:usedFor |
gptkb:data_visualization
nonlinear dimensionality reduction |
| gptkbp:bfsParent |
gptkb:Partha_Niyogi
|
| gptkbp:bfsLayer |
7
|