  We revisit the classical problem of inverting dimension-reducing linear
mappings using the maximum entropy (MaxEnt) criterion. In the literature,
solutions are problem-dependent, inconsistent, and use different entropy
measures. We propose a new unified approach that not only specializes to the
existing approaches, but offers solutions to new cases, such as when data
values are constrained to [0, 1], which has new applications in machine
learning.
