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. ↩
Last update:
2021-03-03