Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals
This work addresses the need for real-time reconstruction of biomedical signals, offering a faster and more accurate solution than existing methods, though it appears incremental as it builds on prior inductive learning techniques.
The paper tackles the problem of reconstructing biomedical signals from lower-dimensional projections by proposing a new inductive learning approach based on Coupled Analysis Dictionary Learning, which achieves faster real-time performance and significantly better reconstruction results compared to traditional Compressed Sensing and Stacked Sparse Denoising Autoencoder methods.
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.