Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls

Yannis Kopsinis; Konstantinos Slavakis; Sergios Theodoridis; S. McLaughlin

doi:10.1109/ICDSP.2011.6005005

Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls

Yannis Kopsinis^*, Konstantinos Slavakis, Sergios Theodoridis, S. McLaughlin

^*Corresponding author for this work

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

This paper presents a novel online method for sparse signal reconstruction. In particular, the notion of sub-dimensional projections is introduced, which allows a significant complexity reduction in the Adaptive Projection-based Algorithm using Weighted ℓ 1 balls (APWL1). This is achieved without sacrificing performance. The proposed method is evaluated in both stationary and time-varying environments and its performance is compared with state-of-the-art online and batch LASSO-based methods.

Original language	English
Title of host publication	17th International Conference on Digital Signal Processing 2011
Publisher	IEEE
ISBN (Electronic)	9781457702747
ISBN (Print)	9781457702730
DOIs	https://doi.org/10.1109/ICDSP.2011.6005005
Publication status	Published - 2011
Event	17th International Conference on Digital Signal Processing 2011 - Corfu, Greece Duration: 6 Jul 2011 → 8 Jul 2011

Conference

Conference	17th International Conference on Digital Signal Processing 2011
Abbreviated title	DSP 2011
Country/Territory	Greece
City	Corfu
Period	6/07/11 → 8/07/11

Keywords

Adaptive filtering
Online signal reconstruction
projections
sparsity

ASJC Scopus subject areas

Signal Processing

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10.1109/ICDSP.2011.6005005

Cite this

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title = "Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls",

abstract = "This paper presents a novel online method for sparse signal reconstruction. In particular, the notion of sub-dimensional projections is introduced, which allows a significant complexity reduction in the Adaptive Projection-based Algorithm using Weighted ℓ 1 balls (APWL1). This is achieved without sacrificing performance. The proposed method is evaluated in both stationary and time-varying environments and its performance is compared with state-of-the-art online and batch LASSO-based methods.",

keywords = "Adaptive filtering, Online signal reconstruction, projections, sparsity",

author = "Yannis Kopsinis and Konstantinos Slavakis and Sergios Theodoridis and S. McLaughlin",

year = "2011",

doi = "10.1109/ICDSP.2011.6005005",

language = "English",

isbn = "9781457702730",

booktitle = "17th International Conference on Digital Signal Processing 2011",

publisher = "IEEE",

address = "United States",

}

Kopsinis, Y, Slavakis, K, Theodoridis, S & McLaughlin, S 2011, Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls. in 17th International Conference on Digital Signal Processing 2011., 6005005, IEEE, 17th International Conference on Digital Signal Processing 2011, Corfu, Greece, 6/07/11. https://doi.org/10.1109/ICDSP.2011.6005005

TY - GEN

T1 - Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls

AU - Kopsinis, Yannis

AU - Slavakis, Konstantinos

AU - Theodoridis, Sergios

AU - McLaughlin, S.

PY - 2011

Y1 - 2011

N2 - This paper presents a novel online method for sparse signal reconstruction. In particular, the notion of sub-dimensional projections is introduced, which allows a significant complexity reduction in the Adaptive Projection-based Algorithm using Weighted ℓ 1 balls (APWL1). This is achieved without sacrificing performance. The proposed method is evaluated in both stationary and time-varying environments and its performance is compared with state-of-the-art online and batch LASSO-based methods.

AB - This paper presents a novel online method for sparse signal reconstruction. In particular, the notion of sub-dimensional projections is introduced, which allows a significant complexity reduction in the Adaptive Projection-based Algorithm using Weighted ℓ 1 balls (APWL1). This is achieved without sacrificing performance. The proposed method is evaluated in both stationary and time-varying environments and its performance is compared with state-of-the-art online and batch LASSO-based methods.

KW - Adaptive filtering

KW - Online signal reconstruction

KW - projections

KW - sparsity

UR - http://www.scopus.com/inward/record.url?scp=80053153733&partnerID=8YFLogxK

U2 - 10.1109/ICDSP.2011.6005005

DO - 10.1109/ICDSP.2011.6005005

M3 - Conference contribution

AN - SCOPUS:80053153733

SN - 9781457702730

BT - 17th International Conference on Digital Signal Processing 2011

PB - IEEE

T2 - 17th International Conference on Digital Signal Processing 2011

Y2 - 6 July 2011 through 8 July 2011

ER -

Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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