A Deep Learning Method Combined Sparse Autoencoder with SVM
Abstract
In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse auto encoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse auto encoder to learn the features of the data. Secondly, we use SVM to classify. Many experimental results show that compared with SVM, our proposed method can improve the classification rate. In particular, it can effectively deal with large-scale data sets.
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Autoencoders can be used as a preprocessing step before a classical classifier or as components within a deep CNN.
“Since they can remove noise and simplify data, AEs can be used as a preprocessing step before a classical classifier (such as a SVM) [75] or even as components within a deep CNN [76,77].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [75]
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References 14 · Cited by 37
- Authors
- Ju, Y., Guo, J., & Liu, S.
- Affiliations
- East China Normal University (CN)
- ORCID
- Guo, Jun
- Venue
- 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 257-260
- OpenAlex
- W1921605540
- Cited by
- 37
- References
- 14
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 3 · 2022: 2 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 7 · 2017: 5
- FWCI
- 2.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- sparse autoencoders, support vector machine, sparse feature learning, unsupervised feature learning, support vector machine classification, big data classification
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