Orange: Manifold Learning
Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html
Nonlinear dimensionality reduction.
Inputs
Data: input dataset
Outputs
Transformed Data: dataset with reduced coordinates
Manifold Learning is a technique which finds a non-linear manifold within the higher-dimensional space. The widget then outputs new coordinates which correspond to a two-dimensional space. Such data can be later visualized with Scatter Plot or other visualization widgets.
Method for manifold learning:
t-SNE
MDS, see also MDS widget
Isomap
Locally Linear Embedding
Spectral Embedding
Set parameters for the method:
t-SNE (distance measures):
Euclidean distance
Manhattan
Chebyshev
Jaccard
Mahalanobis
Cosine
MDS (iterations and initialization):
max iterations: maximum number of optimization interactions
initialization: method for initialization of the algorithm (PCA or random)
Isomap:
number of neighbors
Locally Linear Embedding:
method:
standard
modified
hessian eigenmap
local
number of neighbors
max iterations
Spectral Embedding:
affinity:
nearest neighbors
RFB kernel
Output: the number of reduced features (components).
If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.
Produce a report.
Manifold Learning widget produces different embeddings for high-dimensional data.
From left to right, top to bottom: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding.
Contoh
Manifold Learning widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used voting.tab to map 16-dimensional data onto a 2D graph. Then we used Scatter Plot to plot the embeddings.
