Noisy Deep Dictionary Learning

Abstract

In a recent work, the concept of deep dictionary learning was proposed. Learning a single level of dictionary is a well researched topic in image processing and computer vision community. In deep dictionary learning, the first level proceeds like standard dictionary learning; in subsequent 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 and representations for subsequent analysis and classification. The seminal paper of stacked denoising autoencoders have shown that robust deep models can be learnt when augmented 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 various benchmark datasets on classification and clustering shows that our proposal yields significant improvement.