  This work addresses the problem of reconstructing biomedical signals from
their lower dimensional projections. Traditionally Compressed Sensing (CS)
based techniques have been employed for this task. These are transductive
inversion processes, the problem with these approaches is that the inversion is
time-consuming and hence not suitable for real-time applications. With the
recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE)
has been used for learning inversion in an inductive setup. The training period
for inductive learning is large but is very fast during application -- capable
of real-time speed. This work proposes a new approach for inductive learning of
the inversion process. It is based on Coupled Analysis Dictionary Learning.
Results on Biomedical signal reconstruction show that our proposed approach is
very fast and yields result far better than CS and SSDAE.

Computer Vision and Pattern Recognition