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Comparison study of classification methods for image data
Journal of the Korean Data & Information Science Society 2018;29:267-76
Published online January 31, 2018
© 2018 Korean Data and Information Science Society.

Beom-Jin Park1 · Changyi Park2

12Department of Statistics, University of Seoul
Correspondence to: Professor, Department of Statistics, University of Seoul, Seoul 02504, Korea. E-mail:
Received December 26, 2017; Revised January 8, 2018; Accepted January 9, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Since images are naturally represented as matrices, we have to reshape matrices into vectors in order to apply traditional methods in machine learning to image data. Recently, support matrix machine (SMM) has been proposed to directly classify data matrices without reshaping those matrices into vectors. However, the classification accuracies of SMM and support vector machine were compared in the literature. In this paper, we compare the predictive performance of SMM with those of major classification methods for image data such as k-nearest neighborhood classifier, support vector machine, and deep neural network and understand the characteristics of those learning methods.
Keywords : Deep neural network, support matrix machine, support vector machine.