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.

Referensi

• https://docs.biolab.si//3/visual-programming/widgets/unsupervised/manifoldlearning.html

Pranala Menarik

• Orange