NDLI: Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training

Content Provider	Springer Nature Link
Author	Watanabe, Hideyuki Ohashi, Tsukasa Katagiri, Shigeru Ohsaki, Miho Matsuda, Shigeki Kashioka, Hideki
Copyright Year	2013
Abstract	Recently, one of the standard discriminative training methods for pattern classifier design, i.e., Minimum Classification Error (MCE) training, has been revised, and its new version is called Large Geometric Margin Minimum Classification Error (LGM-MCE) training. It is formulated by replacing a conventional misclassification measure, which is equivalent to the so-called functional margin, with a geometric margin that represents the geometric distance between an estimated class boundary and its closest training pattern sample. It seeks the status of the trainable classifier parameters that simultaneously correspond to the minimum of the empirical average classification error count loss and the maximum of the geometric margin. Experimental evaluations showed the fundamental utility of LGM-MCE training. However, to increase its effectiveness, this new training required careful setting for hyperparameters, especially the smoothness degree of the smooth classification error count loss. Exploring the smoothness degree usually requires many trial-and-error repetitions of training and testing, and such burdensome repetition does not necessarily lead to an optimal smoothness setting. To alleviate this problem and further increase the effect of geometric margin employment, we apply in this paper a new idea that automatically determines the loss smoothness of LGM-MCE training. We first introduce a new formalization of it using the Parzen estimation of error count risk and formalize LGM-MCE training that incorporates a mechanism of automatic loss smoothness determination. Importantly, the geometric-margin-based misclassification measure adopted in LGM-MCE training is directly linked with the geometric margin in a pattern sample space. Based on this relation, we also prove that loss smoothness affects the production of virtual samples along the estimated class boundaries in pattern sample space. Finally, through experimental evaluations and in comparisons with other training methods, we elaborate the characteristics of LGM-MCE training and its new function that automatically determines an appropriate loss smoothness degree.
Starting Page	297
Ending Page	310
Page Count	14
File Format	PDF
ISSN	19398018
Journal	Journal of Signal Processing Systems
Volume Number	74
Issue Number	3
e-ISSN	19398115
Language	English
Publisher	Springer US
Publisher Date	2013-06-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discriminative training Minimum classification error training Robustness to unseen samples Signal, Image and Speech Processing Circuits and Systems Electrical Engineering Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Signal Processing Control and Systems Engineering Information Systems Modeling and Simulation Hardware and Architecture

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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