@inproceedings{703f9db6b45245ab8ee05de5b4f9997c,
title = "Sample Complexity Bounds for Low-Separation-Rank Dictionary Learning",
abstract = "This work addresses the problem of structured dictionary learning for computing sparse representations of tensor-structured data. It introduces a low-separation-rank dictionary learning (LSR-DL) model that better captures the structure of tensor data by generalizing the separable dictionary learning model. A dictionary with p columns that is generated from the LSR-DL model is shown to be locally identifiable from noisy observations with recovery error at most ρ given that the number of training samples scales with (# of degrees of freedom in the dictionary)×p2ρ-2.",
author = "Mohsen Ghassemi and Zahra Shakeri and Bajwa, {Waheed U.} and Sarwate, {Anand D.}",
note = "Funding Information: ACKNOWLEDGEMENT This work is supported in part by the National Science Foundation under awards CCF-1453432 and CCF-1453073, and by the Army Research Office under award W911NF-17-1-0546. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Symposium on Information Theory, ISIT 2019 ; Conference date: 07-07-2019 Through 12-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ISIT.2019.8849698",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2294--2298",
booktitle = "2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings",
address = "United States",
}