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Deterioration level estimation via neural network maximizing category-based ordinally supervised multi-view canonical correlation

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/83370

Title: Deterioration level estimation via neural network maximizing category-based ordinally supervised multi-view canonical correlation
Authors: Maeda, Keisuke Browse this author
Takahashi, Sho Browse this author →KAKEN DB
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Neural network
Within-class divergence
Ordinal scale
Canonical correlation
Deterioration level estimation
Issue Date: 20-Nov-2020
Publisher: Springer
Journal Title: Multimedia tools and applications
Volume: 80
Issue: 15
Start Page: 23091
End Page: 23112
Publisher DOI: 10.1007/s11042-020-10040-2
Abstract: A deterioration level estimation method via neural network maximizing category-based ordinally supervised multi-view canonical correlation is presented in this paper. This paper focuses on real world data such as industrial applications and has two contributions. First, a novel neural network handling multi-modal features transforms original features into features effectively representing deterioration levels in transmission towers, which are one of the infrastructures, with consideration of only correlation maximization. It can be realized by setting projection matrices maximizing correlations between multiple features into weights of hidden layers. That is, since the proposed network has only a few hidden layers, it can be trained from a small amount of training data. Second, since there exist diverse characteristics and an ordinal scale in deterioration levels, the proposed method newly derives category-based ordinally supervised multi-view canonical correlation analysis (Co-sMVCCA). Co-sMVCCA enables estimation of effective projection considering both within-class divergence and the ordinal scale between classes. Experimental results showed that the proposed method realizes accurate deterioration level estimation.
Rights: This is a post-peer-review, pre-copyedit version of an article published in Multimedia tools and applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11042-020-10040-2
Type: article (author version)
URI: http://hdl.handle.net/2115/83370
Appears in Collections:総合IR室 (Office of Institutional Research) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 前田 圭介

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