Auto-Encoder Variants for Solving Handwritten Digits Classification Problem

Muhammad Aamir; Nazri Mohd Nawi; Hairulnizam Bin Mahdin; Rashid Naseem; Muhammad Zulqarnain

doi:10.5391/IJFIS.2020.20.1.8

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저자정보: Muhammad Aamir (Universiti Tun Hussein Onn Malaysia) Nazri Mohd Nawi (Universiti Tun Hussein Onn Malaysia) Hairulnizam Bin Mahdin (Universiti Tun Hussein Onn Malaysia) Rashid Naseem (Pak-Austria Fachhochschule Institute of Applied Sciences and Technology) Muhammad Zulqarnain (Universiti Tun Hussein Onn Malaysia)

저널정보: 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.1

발행연도: 2020.3

수록면: 8 - 16 (9page)

DOI: 10.5391/IJFIS.2020.20.1.8

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Auto-encoders (AEs) have been proposed for solving many problems in the domain of machine learning and deep learning since the last few decades. Due to their satisfactory performance, their multiple variations have also recently appeared. First, we introduce the conventional AE model and its different variant for learning abstract features from data by using a contrastive divergence algorithm. Second, we present the major differences among the following three popular AE variants: sparse AE (SAE), denoising AE (DAE), and contractive AE (CAE). Third, the main contribution of this study is performing the comparative study of the aforementioned three AE variants on the basis of their mathematical modeling and experiments. All the variants of the standard AE are evaluated on the basis of the MNIST benchmark handwritten digit dataset for classification problem. The observed output reveals the benefit of using the AE model and its variants. From the experiments, it is concluded that CAE achieved better classification accuracy than those of SAE and DAE.

#Sparse auto-encoder (SAE) #Denoising auto-encoder (DAE) #Contractive auto-encoder (CAE) #MNIST #Classification

참고문헌 (28)

참고문헌 신청

김병준 / 2018 / A Deep-Learning Based Model for Emotional Evaluation of Video Clips / International Journal of Fuzzy Logic and Intelligent systems 18(4):245~253

F. Wahid / 2019 / Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems / Journal of Intelligent & Fuzzy Systems 36(2):1547~1562

Akmaljon Palvanov / 2018 / Comparisons of Deep Learning Algorithms for MNIST in Real-Time Environment / International Journal of Fuzzy Logic and Intelligent systems 18(2):126~134

M. Aamir / 2017 / A new argumentative based reasoning framework with rough set for decision making / Proceedings of 2017 6th ICT International Student Project Conference (ICT-ISPC) :1~4

N. M. Nawi / 2015 / An accelerated particle swarm optimized back propagation algorithm / Jurnal Teknologi 77(28):49~53

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