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.
Dialog 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.
Ports
Input Ports
0 |
Data table containing the data to map.
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Output Ports
0 |
The input data and the mapped data.
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This node is contained in KNIME Base Nodes
provided by KNIME GmbH, Konstanz, Germany.