Sample Complexity of Dictionary Learning on Stationary Mixing Data

DOI

10.2991/iemb-15.2015.61How to use a DOI?

Keywords

Dictionary learning; Sample complexity; Stationary Mixing; -mixing

Abstract

Dictionary learning is important for many pattern recognition and image processing. Some known jobs focus on the sample complexity of dictionary learning on the independent data for characterizing the performance of a learned dictionary. In this pa-per, the sample complexity of dictionary learning on the stationary mixing input sequence is considered because the stationary mixing input sequence appears in many applications. By discussing the sample complexity of learning dictionary on the -mixing sequence, it has been shown that the better performance of a learned dictionary is a result of controlling the size of a learned dictionary, which means too large size of a learned dictionary will decrease the generalization of the learned dictionary.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guo Li-juan
PY  - 2015/09
DA  - 2015/09
TI  - Sample Complexity of Dictionary Learning on Stationary Mixing Data
BT  - Proceedings of the 2015 Conference on Informatization in Education, Management and Business
PB  - Atlantis Press
SP  - 312
EP  - 318
SN  - 2352-5398
UR  - https://doi.org/10.2991/iemb-15.2015.61
DO  - 10.2991/iemb-15.2015.61
ID  - Li-juan2015/09
ER  -