On Learning Sparsely Used Dictionaries from Incomplete Samples
Abstract
Most existing algorithms for dictionary learning assume that all entries of the (high-dimensional) input data are fully observed. However, in several practical applications (such as hyper-spectral imaging or blood glucose monitoring), only an incomplete fraction of the data entries may be available. For incomplete settings, no provably correct and polynomial-time algorithm has been reported in the dictionary learning literature. In this paper, we provide provable approaches for learning - from incomplete samples - a family of dictionaries whose atoms have sufficiently "spread-out" mass. First, we propose a descent-style iterative algorithm that linearly converges to the true dictionary when provided a sufficiently coarse initial estimate. Second, we propose an initialization algorithm that utilizes a small number of extra fully observed samples to produce such a coarse initial estimate. Finally, we theoretically analyze their performance and provide asymptotic statistical and computational guarantees.
Cite
@article{arxiv.1804.09217,
title = {On Learning Sparsely Used Dictionaries from Incomplete Samples},
author = {Thanh V. Nguyen and Akshay Soni and Chinmay Hegde},
journal= {arXiv preprint arXiv:1804.09217},
year = {2018}
}