NDLI: Representation learning by Denoising Autoencoders for Clustering-based Classification

Content Provider	IEEE Xplore Digital Library
Author	Owhadi-Kareshk, M. Akbarzadeh-T, M.-R.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Eng., Ferdowsi Univ. of Mashhad, Mashhad, Iran (Owhadi-Kareshk, M.; Akbarzadeh-T, M.-R.)
Abstract	Representation learning is a fast growing approach in machine learning that aims to improve the quality of the input data, instead of insisting on designing complex subsequent learning algorithms. In this paper, we propose to use Denoising AutoEncoders (DAEs), as one of the most effective representation learning methods, in Clustering-based Classification (CC). CC is a multi-class classification solution for large-scale and complicated data sets. In this approach, data are divided into small and simple clusters, which are described by One-Class Classifiers (OCCs). In the proposed Representation Learning for Clustering-based Classification (RLCC), the new representation of each cluster is generated locally to increase the performance of OCCs in term of accuracy. This method still preserves the scalability property as one of the significant advantages of CC methods. RLCC is evaluated with six different data sets from UCI. The results of the experiments show that RLCC has higher generalization power compared to the standard version of CC.
Starting Page	228
Ending Page	233
File Size	767946
Page Count	6
File Format	PDF
ISBN	9781467392808
DOI	10.1109/ICCKE.2015.7365832
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-29
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Clustering-based Classification Iris Particle separators Supervised learning Autoencoders Sonar Glass Denoising Autoencoders Representation Learning Representation Learning for Clustering-based Classification Testing
Content Type	Text
Resource Type	Article

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