Deep auto-encoder based clustering

Issue title: 18th Iberoamerican Congress on Pattern Recognition (CIARP) November 20–23, 2013, Havana, Cuba

Guest editors: José Ruiz-Shulcloper and Gabriella Sanniti di Baja

Article type: Research Article

Authors: Song, Chunfeng^a | Huang, Yongzhen^{b; *} | Liu, Feng^c | Wang, Zhenyu^a | Wang, Liang^b

Affiliations: [a] School of Control and Computer, North China Electric Power University, Beijing, China | [b] Institute of Automation, Chinese Academy of Sciences, Beijing, China | [c] School of Automation, Southeast University, Nanjing, Jiangsu, China

Correspondence: [*] Corresponding author: Yongzhen Huang, National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), No. 95 ZhongGuanCun East Street, HaiDian District, Beijing 100190, China. E-mail: yzhuang@nlpr.ia.ac.cn.

Abstract: For unsupervised problems like clustering, linear or non-linear data transformations are widely used techniques. Generally, they are beneficial to data representation. However, if data have a complicated structure, these techniques would be unsatisfying for clustering. In this paper, we propose a new clustering method based on the deep auto-encoder network, which can learn a highly non-linear mapping function. Via simultaneously considering data reconstruction and compactness, our method can obtain stable and effective clustering. Experimental results on four databases demonstrate that the proposed model can achieve promising performance in terms of normalized mutual information, cluster purity and accuracy.

Keywords: Clustering, deep auto-encoder, non-linear transformation, complicated data

DOI: 10.3233/IDA-140709

Journal: Intelligent Data Analysis, vol. 18, no. 6S, pp. S65-S76, 2014