Auto-encoder variants for solving handwritten digits classification problem

Aamir, Muhammad and Mohd Nawi, Nazri and Mahdin, Hairulnizam and Naseem, Rashid and Zulqarnain, Muhammad (2020) Auto-encoder variants for solving handwritten digits classification problem. International Journal of Fuzzy Logic and Intelligent Systems, 20 (1). pp. 8-16. ISSN 1598-2645

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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.

Item Type: Article
Uncontrolled Keywords: Sparse auto-encoder (SAE); Denoising auto-encoder (DAE); Contractive auto-encoder (CAE); MNIST; Classification
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Miss Afiqah Faiqah Mohd Hafiz
Date Deposited: 27 Jan 2022 06:47
Last Modified: 27 Jan 2022 06:47

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