Dimension-Hopping in Machine Learning
Welcome to DataScience.SE! I'd never heard of this problem so I looked it up. It is explained on the third slide of this presentation by Geoff Hinton:


  More things that make it hard to recognize objects
  
  • Changes in viewpoint cause changes in images that standard learning
  methods cannot cope with.
  
  –  Information hops between input dimensions (i.e. pixels)
  
  • Imagine a medical database in which the age of a patient sometimes
  hops to the input dimension that normally codes for weight!
  
  –  To apply machine learning we would first want to eliminate this
  dimension-hopping.


In other words, it is about conceptual features migrating or hopping from one input feature dimension to another while still representing the same thing. One would like to be able to capture or extract the essence of the feature while being invariant to which input dimension it is encoded on.