Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

Zois Boukouvalas; Yuri Levin-Schwartz; Rami Mowakeaa; Geng Shen Fu; Tulay Adali

doi:10.1109/SSP.2018.8450858

Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

Zois Boukouvalas
, Yuri Levin-Schwartz
, Rami Mowakeaa
, Geng Shen Fu
, Tulay Adali

Icahn School of Medicine at Mount Sinai

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

7 Scopus citations

Abstract

Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources. We present a mathematical justification of its convergence and demonstrate its superior performance over competing ICA algorithms using simulated as well as real-world data.

Original language	English
Title of host publication	2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	55-59
Number of pages	5
ISBN (Print)	9781538615706
DOIs	https://doi.org/10.1109/SSP.2018.8450858
State	Published - 29 Aug 2018
Event	20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany Duration: 10 Jun 2018 → 13 Jun 2018

Publication series

Name	2018 IEEE Statistical Signal Processing Workshop, SSP 2018

Conference

Conference	20th IEEE Statistical Signal Processing Workshop, SSP 2018
Country/Territory	Germany
City	Freiburg im Breisgau
Period	10/06/18 → 13/06/18

Keywords

Independent component analysis
entropy maximization

Access to Document

10.1109/SSP.2018.8450858

Cite this

Boukouvalas, Z., Levin-Schwartz, Y., Mowakeaa, R., Fu, G. S., & Adali, T. (2018). Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization. In 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 (pp. 55-59). Article 8450858 (2018 IEEE Statistical Signal Processing Workshop, SSP 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSP.2018.8450858

@inproceedings{ac8178ce32174f98bd86e843ae242a0e,

title = "Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization",

abstract = "Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources. We present a mathematical justification of its convergence and demonstrate its superior performance over competing ICA algorithms using simulated as well as real-world data.",

keywords = "Independent component analysis, entropy maximization",

author = "Zois Boukouvalas and Yuri Levin-Schwartz and Rami Mowakeaa and Fu, \{Geng Shen\} and Tulay Adali",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 20th IEEE Statistical Signal Processing Workshop, SSP 2018 ; Conference date: 10-06-2018 Through 13-06-2018",

year = "2018",

month = aug,

day = "29",

doi = "10.1109/SSP.2018.8450858",

language = "English",

isbn = "9781538615706",

series = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "55--59",

booktitle = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",

address = "United States",

}

Boukouvalas, Z, Levin-Schwartz, Y, Mowakeaa, R, Fu, GS & Adali, T 2018, Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization. in 2018 IEEE Statistical Signal Processing Workshop, SSP 2018., 8450858, 2018 IEEE Statistical Signal Processing Workshop, SSP 2018, Institute of Electrical and Electronics Engineers Inc., pp. 55-59, 20th IEEE Statistical Signal Processing Workshop, SSP 2018, Freiburg im Breisgau, Germany, 10/06/18. https://doi.org/10.1109/SSP.2018.8450858

Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization. / Boukouvalas, Zois; Levin-Schwartz, Yuri; Mowakeaa, Rami et al.
2018 IEEE Statistical Signal Processing Workshop, SSP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 55-59 8450858 (2018 IEEE Statistical Signal Processing Workshop, SSP 2018).

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

TY - GEN

T1 - Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

AU - Boukouvalas, Zois

AU - Levin-Schwartz, Yuri

AU - Mowakeaa, Rami

AU - Fu, Geng Shen

AU - Adali, Tulay

PY - 2018/8/29

Y1 - 2018/8/29

N2 - Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources. We present a mathematical justification of its convergence and demonstrate its superior performance over competing ICA algorithms using simulated as well as real-world data.

AB - Independent component analysis (ICA) is one of the most popular methods for blind source separation with a diverse set of applications, such as: biomedical signal processing, video and image analysis, and communications. The success of ICA is tied to proper characterization of the probability density function (PDF) of the latent sources; information that is generally unknown. In this work, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources. We present a mathematical justification of its convergence and demonstrate its superior performance over competing ICA algorithms using simulated as well as real-world data.

KW - Independent component analysis

KW - entropy maximization

UR - https://www.scopus.com/pages/publications/85053845456

U2 - 10.1109/SSP.2018.8450858

DO - 10.1109/SSP.2018.8450858

M3 - Conference contribution

AN - SCOPUS:85053845456

SN - 9781538615706

T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

SP - 55

EP - 59

BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018

Y2 - 10 June 2018 through 13 June 2018

ER -

Boukouvalas Z, Levin-Schwartz Y, Mowakeaa R, Fu GS, Adali T. Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization. In 2018 IEEE Statistical Signal Processing Workshop, SSP 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 55-59. 8450858. (2018 IEEE Statistical Signal Processing Workshop, SSP 2018). doi: 10.1109/SSP.2018.8450858

Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this