MDS

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.

Options

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.

Epochs

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.

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Workflows

• 00_Topic_Modeling_with_Verified_ComponentsKNIME Hub
• 00_Topic_Modeling_with_Verified_ComponentsKNIME Hub
• 03_Topic_Explorer_ExampleKNIME Hub
• 06_Topic_Modeling_LDA_Data_AppKNIME Hub
• 07_Topic_Modeling_STM_Data_AppKNIME Hub
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