Max-dependence regression

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


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 languageEnglish
Title of host publicationProceedings of the 2014 International Joint Conference on Neural Networks
EditorsCesare Alippi
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781479914845, 9781479966271
ISBN (Print)9781479914821
Publication statusPublished - 3 Sep 2014
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2014
Abbreviated titleIJCNN 2014
Internet address

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