|
gptkbp:instanceOf
|
dimensionality reduction algorithm
|
|
gptkbp:application
|
exploratory data analysis
visualization of high-dimensional data
|
|
gptkbp:citation
|
over 50,000
|
|
gptkbp:field
|
gptkb:machine_learning
gptkb:data_visualization
|
|
gptkbp:form
|
gptkb:Kullback-Leibler_divergence
probability distributions
|
|
gptkbp:fullName
|
gptkb:t-distributed_Stochastic_Neighbor_Embedding
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
t-SNE
|
|
gptkbp:hyperparameter
|
learning rate
number of iterations
perplexity
|
|
gptkbp:implementedIn
|
gptkb:TensorFlow
gptkb:MATLAB
gptkb:scikit-learn
Rtsne
|
|
gptkbp:input
|
high-dimensional data
|
|
gptkbp:inventedBy
|
gptkb:Geoffrey_Hinton
gptkb:Laurens_van_der_Maaten
|
|
gptkbp:limitation
|
computationally expensive
does not preserve global structure
non-deterministic results
|
|
gptkbp:measures
|
Euclidean distance
|
|
gptkbp:openSource
|
true
|
|
gptkbp:optimizedFor
|
gradient descent
|
|
gptkbp:originalPaperTitle
|
Visualizing Data using t-SNE
|
|
gptkbp:originalPaperURL
|
https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
|
|
gptkbp:output
|
low-dimensional embedding
|
|
gptkbp:outputDimension
|
2
3
|
|
gptkbp:publicationYear
|
2008
|
|
gptkbp:supportsAlgorithm
|
gptkb:LLE
gptkb:Isomap
gptkb:UMAP
gptkb:PCA
|
|
gptkbp:usedFor
|
clustering visualization
image data visualization
manifold learning
single-cell RNA-seq analysis
word embedding visualization
|
|
gptkbp:bfsParent
|
gptkb:principal_component_analysis
|
|
gptkbp:bfsLayer
|
5
|