However, our extensive experiments with this approach (omitted here) show that it suffers from a number of flaws, such as a high false positive rate if all features are tracked, the difficult problem of identifying an informative subset of features for tracking, and deciding how many such features need to change before a shift has occurred, which turns out to be highly variable between shifts.
our goal is to use a single A-distance tracker by collapsing each example to a single value.