In a time when data increase massively in their volume, variety, and velocity, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper proposes a data-driven measurement selection scheme to render tracking of large-scale dynamic processes affordable, by processing a reduced number of data. The proposed method processes observations sequentially, and extracts a low-complexity sketch that can be implemented in real-time. Furthermore, a low-complexity smoothing is developed as a means of mitigating the error performance degradation caused by dimensionality reduction. Simulations on synthetic data, compare the proposed methods with competing alternatives, and corroborate their efficacy in terms of estimation accuracy versus complexity reduction.
|Original language||English (US)|
|Title of host publication||Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Feb 26 2016|
|Event||49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States|
Duration: Nov 8 2015 → Nov 11 2015
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Other||49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015|
|Period||11/8/15 → 11/11/15|
Bibliographical noteFunding Information:
Work in this paper was supported by NSF Grants No. 1514056 and 1500713, and NIH Grant No. 1R01GM104975-01
© 2015 IEEE.