  This paper studies the application of machine learning in extracting the
market implied features from historical risk neutral corporate bond yields. We
consider the example of a hypothetical illiquid fixed income market. After
choosing a surrogate liquid market, we apply the Denoising Autoencoder
algorithm from the field of computer vision and pattern recognition to learn
the features of the missing yield parameters from the historically implied data
of the instruments traded in the chosen liquid market. The results of the
trained machine learning algorithm are compared with the outputs of a point in-
time 2 dimensional interpolation algorithm known as the Thin Plate Spline.
Finally, the performances of the two algorithms are compared.
