Autoencoders and their applications in machine learning: a survey

Berahmand, Kamal; Daneshfar, Fatemeh; Salehi, Elaheh Sadat; Li, Yuefeng; Xu, Yue

doi:10.1007/s10462-023-10662-6

Cited by 71 publications

(8 citation statements)

References 191 publications

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“…Introducing knowledge graphs into recommendation systems could potentially enrich the system with a broader range of information, thereby uncovering more latent relationships and achieving superior performance. On the other hand, incorporating additional information into recommendation systems may pose challenges, such as compromising model robustness and introducing excessive noise [19]. In our future research endeavors, we aim to broaden our focus beyond solely self-supervised learning of graphs.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, some GNN-based recommendation systems have also aimed to employ self-supervision signals from autoencoders [18][19][20] or contrastive learning-based approaches [21,22]. Contrastive learning typically constructs pairs of positive and negative samples through data augmentation methods and then forces positive samples closer to and negative samples further from each other, thereby maximizing the discrimination between positive and negative pairs for representation learning.…”

Section: Introductionmentioning

confidence: 99%

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SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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As user–item interaction information is typically limited, collaborative filtering (CF)-based recommender systems often suffer from the data sparsity issue. To address this issue, recent recommender systems have turned to graph neural networks (GNNs) due to their superior performance in capturing high-order relationships. Furthermore, some of these GNN-based recommendation models also attempt to incorporate other information. They either extract self-supervised signals to mitigate the data sparsity problem or employ social information to assist with learning better representations under a social recommendation setting. However, only a few methods can take full advantage of these different aspects of information. Based on some testing, we believe most of these methods are complex and redundantly designed, which may lead to sub-optimal results. In this paper, we propose SSGCL, which is a recommendation system model that utilizes both social information and self-supervised information. We design a GNN-based propagation strategy that integrates social information with interest information in a simple yet effective way to learn user–item representations for recommendations. In addition, a specially designed contrastive learning module is employed to take advantage of the self-supervised signals for a better user–item representation distribution. The contrastive learning module is jointly optimized with the recommendation module to benefit the final recommendation result. Experiments on several benchmark data sets demonstrate the significant improvement in performance achieved by our model when compared with baseline models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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“…The autoencoder [21,22] is an unsupervised learning technique that utilizes backpropagation and optimization methods to learn a mapping relationship from input data (referred to as "data") to guide the neural network in generating a reconstructed output X that is similar or identical to the input.…”

Section: Ae (Autoencoder)mentioning

confidence: 99%

Research on the Simulation Method of HTTP Traffic Based on GAN

Yang,

Xu,

2024

Applied Sciences

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Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced a network traffic data normalization method based on Gaussian mixture models (GMM), and for the first time, incorporated a generator based on the Swin Transformer structure into the field of network traffic generation. To further enhance the robustness of the model, we mapped real data through an AE (autoencoder) module and optimized the training results in the form of evolutionary algorithms. We validated the training results on four different datasets and introduced four additional models for comparative experiments in the experimental evaluation section. Our proposed SEGAN outperformed other state-of-the-art network traffic emulation methods.

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“…Finally, they carried out experiments and obtained an average of F-measure metric over 86%. With the development of deep learning technology, autoencoder models have attracted the attention of many researchers and have been applied in many fields [36][37][38]. Meanwhile, researchers have proposed improved models for traditional autoencoders, such as noise reduction autoencoders, convolutional autoencoders, and stacked autoencoders.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

Zhou,

He,

Chen

et al. 2024

Aerospace

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The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

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Autoencoders and their applications in machine learning: a survey

Cited by 71 publications

References 191 publications

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Research on the Simulation Method of HTTP Traffic Based on GAN

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

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