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Volume 5, Issue 1, June 2018, Pages 75 - 78
Unsupervised Image Classification Using Multi-Autoencoder and K-means++
Authors
Shingo Mabumabu@yamaguchi-u.ac.jp
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1 Ube, Yamaguchi 755-8611, Japan
Kyoichiro Kobayashi
Department of Information Science and Engineering, Faculty of Engineering, Yamaguchi University, Tokiwadai 2-16-1 Ube, Yamaguchi 755-8611, Japan
Masanao Obayashim.obayas@yamaguchi-u.ac.jp
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1 Ube, Yamaguchi 755-8611, Japan
Takashi Kuremotowu@yamaguchi-u.ac.jp
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1 Ube, Yamaguchi 755-8611, Japan
Available Online 30 June 2018.
- DOI
- 10.2991/jrnal.2018.5.1.17How to use a DOI?
- Keywords
- neural network; deep autoencoder; K-means++; clustering
- Abstract
Supervised learning algorithms such as deep neural networks have been actively applied to various problems. However, in image classification problem, for example, supervised learning needs a large number of data with correct labels. In fact, the cost of giving correct labels to the training data is large; therefore, this paper proposes an unsupervised image classification system with Multi-Autoencoder and K-means++ and evaluates its performance using benchmark image datasets.
- Copyright
- Copyright © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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Cite this article
TY - JOUR AU - Shingo Mabu AU - Kyoichiro Kobayashi AU - Masanao Obayashi AU - Takashi Kuremoto PY - 2018 DA - 2018/06/30 TI - Unsupervised Image Classification Using Multi-Autoencoder and K-means++ JO - Journal of Robotics, Networking and Artificial Life SP - 75 EP - 78 VL - 5 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2018.5.1.17 DO - 10.2991/jrnal.2018.5.1.17 ID - Mabu2018 ER -