TY - GEN
T1 - Tractable Learning of Sparsely Used Dictionaries from Incomplete Samples
AU - Nguyen, Thanh V.
AU - Soni, Akshay
AU - Hegde, Chinmay
N1 - 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.
UR - http://www.scopus.com/inward/record.url?scp=85082861013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082861013&partnerID=8YFLogxK
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 -