0 ×

KNFST Learner

KNIME Active Learning Plug-in version 4.3.0.v202011190949 by KNIME AG, Zurich, Switzerland

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


To use this node in KNIME, install KNIME Active Learning from the following update site:


A zipped version of the software site can be downloaded here.

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

Wait a sec! You want to explore and install nodes even faster? We highly recommend our NodePit for KNIME extension for your KNIME Analytics Platform. Browse NodePit from within KNIME, install nodes with just one click and share your workflows with NodePit Space.


You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.