TY - GEN
T1 - Tractable Learning of Sparsely Used Dictionaries from Incomplete Samples
AU - Nguyen, Thanh V.
AU - Soni, Akshay
AU - Hegde, Chinmay
N1 - Funding Information:
*Email: {thanhng, chinmay}@iastate.edu; akson@microsoft.com. T. N. and C. H. are with the Electrical and Computer Engineering Department at Iowa State University. A. S. is with Microsoft. This work was supported in part by the National Science Foundation under grants CCF-1566281 and CCF-1750920, and in part by a Faculty Fellowship from the Black and Veatch Foundation.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In dictionary learning, we seek a collection of atoms that sparsely represent a given set of training samples. While this problem is well-studied, relatively less is known about the more challenging case where the samples are incomplete, i.e., we only observe a fraction of their coordinates. In this paper, we develop and analyze an algorithm to solve this problem, provided that the dictionary satisfies additional low-dimensional structure.
AB - In dictionary learning, we seek a collection of atoms that sparsely represent a given set of training samples. While this problem is well-studied, relatively less is known about the more challenging case where the samples are incomplete, i.e., we only observe a fraction of their coordinates. In this paper, we develop and analyze an algorithm to solve this problem, provided that the dictionary satisfies additional low-dimensional structure.
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U2 - 10.1109/SampTA45681.2019.9030979
DO - 10.1109/SampTA45681.2019.9030979
M3 - Conference contribution
AN - SCOPUS:85082861013
T3 - 2019 13th International Conference on Sampling Theory and Applications, SampTA 2019
BT - 2019 13th International Conference on Sampling Theory and Applications, SampTA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Sampling Theory and Applications, SampTA 2019
Y2 - 8 July 2019 through 12 July 2019
ER -