  Powerful deep learning algorithms open an opportunity for solving non-image
Machine Learning (ML) problems by transforming these problems to into the image
recognition problems. The CPC-R algorithm presented in this chapter converts
non-image data into images by visualizing non-image data. Then deep learning
CNN algorithms solve the learning problems on these images. The design of the
CPC-R algorithm allows preserving all high-dimensional information in 2-D
images. The use of pair values mapping instead of single value mapping used in
the alternative approaches allows encoding each n-D point with 2 times fewer
visual elements. The attributes of an n-D point are divided into pairs of its
values and each pair is visualized as 2-D points in the same 2-D Cartesian
coordinates. Next, grey scale or color intensity values are assigned to each
pair to encode the order of pairs. This is resulted in the heatmap image. The
computational experiments with CPC-R are conducted for different CNN
architectures, and methods to optimize the CPC-R images showing that the
combined CPC-R and deep learning CNN algorithms are able to solve non-image ML
problems reaching high accuracy on the benchmark datasets. This chapter expands
our prior work by adding more experiments to test accuracy of classification,
exploring saliency and informativeness of discovered features to test their
interpretability, and generalizing the approach.
