Dimensionality reduction

Dimensionality reduction is an unsupervised machine learning (ML) technique that reduces the number of features in a dataset while preserving its meaningful structure and relationships.

PCA

Principal Component Analysis (PCA) captures the most significant patterns in the data by transforming it into a new coordinate system to maximize variance along orthogonal axes.

• Open a table
• Run Top Menu > ML > Dimensionality reduction > PCA...
• Select the source table and Feature columns
• Set the number of principal Components
• Set Center and/or Scale data pre-processing options
• Press OK

Datagrok ensures blazingly fast computations:

See also:

• PCA

UMAP

Uniform Manifold Approximation and Projection (UMAP) is a nonlinear method for mapping high-dimensional data to a lower-dimensional space preserving its global and local structures.

• Open a table
• Run Top Menu > ML > Dimensionality reduction > UMAP...
• Select the source table and Feature columns
• Set Hyperparameters and press OK

Use scatter plot and/or 3D scatter plot to visualize results:

See also:

• UMAP

t-SNE

t-distributed stochastic neighbor embedding (t-SNE) reveals the underlying complex data structure by representing its similar points as nearby neighbors in a lower-dimensional space.

• Open a table
• Run Top Menu > ML > Dimensionality reduction > t-SNE...
• Select the source table and Feature columns
• Set Hyperparameters and press OK

See also:

• t-SNE

SPE

Stochastic proximity embedding (SPE) is a self-organizing method that produces meaningful underlying dimensions from proximity data.

• Open a table
• Run Top Menu > ML > Dimensionality reduction > SPE...
• Select the source table and Feature columns
• Set Hyperparameters and press OK

See also:

• SPE