Manifold Learning

Nonlinear dimensionality reduction.


  • Data: input dataset


  • 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.

  1. Method for manifold learning:
  2. 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:
      • number of neighbors
      • max iterations
    • Spectral Embedding:
      • affinity:
        • nearest neighbors
        • RFB kernel
  3. Output: the number of reduced features (components).
  4. If Apply automatically is ticked, changes will be propagated automatically. Alternatively, click Apply.
  5. 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.


All projections use default preprocessing if necessary. It is executed in the following order:

  • continuization of categorical variables (with one feature per value)
  • imputation of missing values with mean values

To override default preprocessing, preprocess the data beforehand with Preprocess widget.


Manifold Learning widget transforms high-dimensional data into a lower dimensional approximation. This makes it great for visualizing datasets with many features. We used to map 16-dimensional data onto a 2D graph. Then we used Scatter Plot to plot the embeddings.

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