GS4-3 Unsupervised Image Classification Using Multi-Autoencoder and K-means++

Title:	GS4-3 Unsupervised Image Classification Using Multi-Autoencoder and K-means++
Publication:	ICAROB2018
Volume:	23
Pages:	112-115
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2018.GS4-3
Author(s):	Shingo Mabu, Kyoichiro Kobayashi, Masanao Obayashi, Takashi Kuremoto
Publication Date:	February 2, 2018
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.
PDF File:	https://alife-robotics.co.jp/members2018/icarob/data/html/data/GS_pdf/GS4/GS4-3.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/