This node maps data of a high dimensional space onto a lower (usually 2 or 3) dimensional space. Therefore the Sammons mapping is applied, which iteratively decreases the difference of the distances of high and low dimensional data. Each original data point is represented by a data point of a lower dimension. The Sammons mapping tries to keep the distance information of the high dimensional data by adjusting the low dimensional data points in a certain way. Each low dimensional data point is moved around a bit towards or back from the other points accordant to its high dimensional distances. This procedure is repeated a specified number of epochs or iterations respectively.


Number of rows to use
Specifies the number of rows to apply the MDS on.
Output dimension
Specifies the dimension of the mapped output data.
Specifies the number of epochs to train.
Learn rate
Specifies the learning rate to use. The learning rate is decreased automatically over the trained epochs.
Random seed
Specifies the random seed to use, which allows to reproduce a mapping even if the initialization is done randomly.
Distance metric
The distance metric to use Euclidean or Manhattan. The Euclidean distance metric is used by default.
Input data
Specifies the columns to use by the mapping.

Input Ports

Data table containing the data to map.

Output Ports

The input data and the mapped data.


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