Noisy deep dictionary learning: Application to Alzheimer's Disease classification

Abstract

A recent work introduced the concept of deep dictionary learning. In deep dictionary learning, the first level proceeds like standard dictionary learning; in sub-sequent layers the (scaled) output coefficients from the previous layer are used as inputs for dictionary learning. This is an unsupervised deep learning approach. The features from the final / deepest layer are employed for subsequent analysis and classification. The seminal paper of stacked denoising autoencoders have shown that robust deep models can be learnt when noisy data is used for training stacked autoencoders instead of clean data. We adopt this idea into the deep dictionary learning framework; instead of using only clean data we augment the training dataset by adding noise; this improves robustness. Experimental evaluation on benchmark deep learning datasets and real world problem of AD classification show that our proposal yields considerable improvement.