  The volume of data processed by the Large Hadron Collider experiments demands
sophisticated selection rules typically based on machine learning algorithms.
One of the shortcomings of these approaches is their profound sensitivity to
the biases in training samples. In the case of particle identification (PID),
this might lead to degradation of the efficiency for some decays not present in
the training dataset due to differences in input kinematic distributions. In
this talk, we propose a method based on the Common Specific Decomposition that
takes into account individual decays and possible misshapes in the training
data by disentangling common and decay specific components of the input feature
set. We show that the proposed approach reduces the rate of efficiency
degradation for the PID algorithms for the decays reconstructed in the LHCb
detector.
