KNFST Learner

Calculates a Kernel Null Foley-Sammon Transformation that can be used to calculate a novelty score for a test sample in relation to the training samples.

The corresponding source code is a java adaption of the Matlab code provided by Bodesheim et al. for their paper "Kernel Null Space Methods for Novelty Detection".


Training Columns
The columns which should be used for training (right now only numeric columns can be used for training)
Class Column
The column containing the class of the training samples
Choose the Kernel that should be used
  • RBF - Radial basis function kernel (with parameter sigma)
  • HIK - Histogram intersection kernel
  • EXPHIK - Exponential variant of the HIK
  • Polynomial - Polynomial kernel (with parameters gamma, bias and sigma)
Sort Table
If you check this option, the input table will be sorted by class prior to calculating the nullspace. If it already is sorted by class, don't use this option.

Input Ports

Training data

Output Ports

Target points. Each class of the input table is represented by one point in the nullspace to which all instances of that class are projected.
KNFST-model that can be used by a KNFST Novelty Scorer node to rate the novelty of instances

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