Max-dependence regression

Pouria Fewzee; Ali Akbar Samadani; Dana Kulic; Fakhri Karray

doi:10.1109/IJCNN.2014.6889867

Max-dependence regression

Pouria Fewzee, Ali Akbar Samadani, Dana Kulic, Fakhri Karray

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

Abstract

This work proposes an approach for solving the linear regression problem by maximizing the dependence between prediction values and the response variable. The proposed algorithm uses the Hilbert-Schmidt independence criterion as a generic measure of dependence and can be used to maximize both nonlinear and linear dependencies. The algorithm is important in applications such as continuous analysis of affective speech, where linear dependence, or correlation, is commonly set as the measure of goodness of fit. The applicability of the proposed algorithm is verified using two synthetic, one affective speech, and one affective bodily posture datasets. Experimental results show that the proposed algorithm outperforms support vector regression (SVR) in 84% (264/314) of studied cases, and is noticeably faster than SVR, as an order of 25, on average.

Original language	English
Title of host publication	Proceedings of the 2014 International Joint Conference on Neural Networks
Editors	Cesare Alippi
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1652-1659
Number of pages	8
ISBN (Electronic)	9781479914845, 9781479966271
ISBN (Print)	9781479914821
DOIs	https://doi.org/10.1109/IJCNN.2014.6889867
Publication status	Published - 3 Sept 2014
Externally published	Yes
Event	IEEE International Joint Conference on Neural Networks 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding (Proceedings)

Conference

Conference	IEEE International Joint Conference on Neural Networks 2014
Abbreviated title	IJCNN 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding (Proceedings)

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10.1109/IJCNN.2014.6889867

Cite this

@inproceedings{de62a3ac14a645c8aa93ffbaa0630256,

title = "Max-dependence regression",

abstract = "This work proposes an approach for solving the linear regression problem by maximizing the dependence between prediction values and the response variable. The proposed algorithm uses the Hilbert-Schmidt independence criterion as a generic measure of dependence and can be used to maximize both nonlinear and linear dependencies. The algorithm is important in applications such as continuous analysis of affective speech, where linear dependence, or correlation, is commonly set as the measure of goodness of fit. The applicability of the proposed algorithm is verified using two synthetic, one affective speech, and one affective bodily posture datasets. Experimental results show that the proposed algorithm outperforms support vector regression (SVR) in 84% (264/314) of studied cases, and is noticeably faster than SVR, as an order of 25, on average.",

author = "Pouria Fewzee and Samadani, {Ali Akbar} and Dana Kulic and Fakhri Karray",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/IJCNN.2014.6889867",

language = "English",

isbn = "9781479914821",

pages = "1652--1659",

editor = "Cesare Alippi",

booktitle = "Proceedings of the 2014 International Joint Conference on Neural Networks",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Joint Conference on Neural Networks 2014, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

url = "https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding",

}

Fewzee, P, Samadani, AA, Kulic, D & Karray, F 2014, Max-dependence regression. in C Alippi (ed.), Proceedings of the 2014 International Joint Conference on Neural Networks. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 1652-1659, IEEE International Joint Conference on Neural Networks 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889867

Max-dependence regression. / Fewzee, Pouria; Samadani, Ali Akbar; Kulic, Dana et al.
Proceedings of the 2014 International Joint Conference on Neural Networks. ed. / Cesare Alippi. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 1652-1659.

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

TY - GEN

T1 - Max-dependence regression

AU - Fewzee, Pouria

AU - Samadani, Ali Akbar

AU - Kulic, Dana

AU - Karray, Fakhri

PY - 2014/9/3

Y1 - 2014/9/3

N2 - This work proposes an approach for solving the linear regression problem by maximizing the dependence between prediction values and the response variable. The proposed algorithm uses the Hilbert-Schmidt independence criterion as a generic measure of dependence and can be used to maximize both nonlinear and linear dependencies. The algorithm is important in applications such as continuous analysis of affective speech, where linear dependence, or correlation, is commonly set as the measure of goodness of fit. The applicability of the proposed algorithm is verified using two synthetic, one affective speech, and one affective bodily posture datasets. Experimental results show that the proposed algorithm outperforms support vector regression (SVR) in 84% (264/314) of studied cases, and is noticeably faster than SVR, as an order of 25, on average.

AB - This work proposes an approach for solving the linear regression problem by maximizing the dependence between prediction values and the response variable. The proposed algorithm uses the Hilbert-Schmidt independence criterion as a generic measure of dependence and can be used to maximize both nonlinear and linear dependencies. The algorithm is important in applications such as continuous analysis of affective speech, where linear dependence, or correlation, is commonly set as the measure of goodness of fit. The applicability of the proposed algorithm is verified using two synthetic, one affective speech, and one affective bodily posture datasets. Experimental results show that the proposed algorithm outperforms support vector regression (SVR) in 84% (264/314) of studied cases, and is noticeably faster than SVR, as an order of 25, on average.

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DO - 10.1109/IJCNN.2014.6889867

M3 - Conference Paper

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SP - 1652

EP - 1659

BT - Proceedings of the 2014 International Joint Conference on Neural Networks

A2 - Alippi, Cesare

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T2 - IEEE International Joint Conference on Neural Networks 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

Max-dependence regression

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