  In this paper, we evaluate dimensionality reduction methods in terms of
difficulty in estimating visual information on original images from
dimensionally reduced ones. Recently, dimensionality reduction has been
receiving attention as the process of not only reducing the number of random
variables, but also protecting visual information for privacy-preserving
machine learning. For such a reason, difficulty in estimating visual
information is discussed. In particular, the random sampling method that was
proposed for privacy-preserving machine learning, is compared with typical
dimensionality reduction methods. In an image classification experiment, the
random sampling method is demonstrated not only to have high difficulty, but
also to be comparable to other dimensionality reduction methods, while
maintaining the property of spatial information invariant.
