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Authors: Ozde Tiryaki 1 and C. Okan Sakar 2

Affiliations: 1 Department of Computer Engineering, Bahcesehir University, Istanbul, Turkey, NETAS Telecommunication Company, Kurtkoy, Istanbul and Turkey ; 2 Department of Computer Engineering, Bahcesehir University, Istanbul and Turkey

ISBN: 978-989-758-318-6

Keyword(s): Alternating Regression (Ar), Multiple-output Regression, Neural Networks, Kernel Canonical Correlation Analysis (Kcca), Nonlinear Dimensionality Reduction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Canonical Correlation Analysis (CCA) is a data analysis technique used to extract correlated features between two sets of variables. An important limitation of CCA is that it is a linear technique that cannot capture nonlinear relations in complex situations. To address this limitation, Kernel CCA (KCCA) has been proposed which is capable of identifying the nonlinear relations with the use of kernel trick. However, it has been shown that KCCA tends to overfit to the training set without proper regularization. Besides, KCCA is an unsupervised technique which does not utilize class labels for feature extraction. In this paper, we propose the nonlinear version of the discriminative alternating regression (D-AR) method to address these problems. While in linear D-AR two neural networks each with a linear bottleneck hidden layer are combined using alternating regression approach, the modified version of the linear D-AR proposed in this study has a nonlinear activation function in the hidde n layers of the alternating multilayer perceptrons (MLP). Experimental results on a classification and a multiple-output regression problem with sigmoid and hyperbolic tangent activation functions show that features found by nonlinear D-AR from training examples accomplish significantly higher accuracy on test set than that of KCCA. (More)

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Paper citation in several formats:
Tiryaki, O. and Okan Sakar, C. (2018). Nonlinear Feature Extraction using Multilayer Perceptron based Alternating Regression for Classification and Multiple-output Regression Problems.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 107-117. DOI: 10.5220/0006848901070117

@conference{data18,
author={Ozde Tiryaki. and C. Okan Sakar.},
title={Nonlinear Feature Extraction using Multilayer Perceptron based Alternating Regression for Classification and Multiple-output Regression Problems},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={107-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006848901070117},
isbn={978-989-758-318-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Nonlinear Feature Extraction using Multilayer Perceptron based Alternating Regression for Classification and Multiple-output Regression Problems
SN - 978-989-758-318-6
AU - Tiryaki, O.
AU - Okan Sakar, C.
PY - 2018
SP - 107
EP - 117
DO - 10.5220/0006848901070117

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