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Safe Euclidean#

An Euclidean distance metric suitable for samples that may contain NaN (not a number) values i.e. missing data. The Safe Euclidean metric approximates the Euclidean distance function by dropping NaN values and scaling the distance according to the proportion of non-NaNs (in either a or b or both) to compensate.

Data Type Compatibility: Continuous

Parameters#

This kernel does not have any parameters.

Example#

use Rubix\ML\Kernels\Distance\SafeEuclidean;

$kernel = new SafeEuclidean();

References#

  • J. K. Dixon. (1978). Pattern Recognition with Partly Missing Data.