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Taxonomy-aware collaborative denoising autoencoder for personalized recommendation

APPLIED INTELLIGENCE 2019年第6期49卷 2101-2118页

作者： Zhang, Chunhong Li, Tiantian Ren, Zhibin Hu, Zheng Ji, Yang Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.

关键词： Recommender systems Taxonomy denoising autoencoder Collaborative filtering

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A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 196卷

作者： Lu, Yu Yang, Tao Zhao, Chong Chen, Wen Zeng, Rong China West Normal Univ Sch Comp Sci Nanchong Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China China West Normal Univ Sch Elect Informat Engn Nanchong Peoples R China

The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise-removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.

关键词： UAV swarm Intrusion detection Federated learning denoising autoencoder

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An enhancement denoising autoencoder for rolling bearing fault diagnosis

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MEASUREMENT 2018年 130卷 448-454页

作者： Meng, Zong Zhan, Xuyang Li, Jing Pan, Zuozhou Yanshan Univ Qinhuangdao Peoples R China

denoising autoencoders can automatically learn in-depth features from complex data and extract concise expressions, which are used in fault diagnosis. However, they still have many drawbacks: (1) unsatisfactory results when the input data is not substantial;(2) difficulty in optimising the hyperparameter;(3) inability of existing regularisation methods to combine the advantages of L1 and L2 regularisation. To overcome the aforementioned challenges, here, a new data preprocessing method was proposed to obtain the training data. By reusing the data points between the adjacent samples, the fault identifying rate was significantly improved. Considering the different resilience of each layer after regularisation, the proposed method could alter the hyperparameter by changing the unit numbers of each layer. For a better sparse representation, the norm penalty combined L1 and L2 norm penalties, motivated by the elastic net. Comparison with a normal denoising autoencoder verified the superiority of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： denoising autoencoder Fault diagnosis Hyperparameter Regularisation Elastic net

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Feature learning using convolutional denoising autoencoder for activity recognition

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10909-10922页

作者： Mohd Noor, Mohd Halim Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Wearable technology offers a prospective solution to the increasing demand for activity monitoring in pervasive healthcare. Feature extraction and selection are crucial steps in activity recognition since it determines the accuracy of activity classification. However, existing feature extraction and selection methods involve manual feature engineering, which is time-consuming, laborious and prone to error. Therefore, this paper proposes an unsupervised feature learning method that automatically extracts and selects the features without human intervention. Specifically, the proposed method jointly trains a convolutional denoising autoencoder with a convolutional neural network to learn the underlying features and produces a compact feature representation of the data. This allows not only more accurate and discriminative features to be extracted but also reduces the computational cost and improves generalization of the classification models. The proposed method was evaluated and compared with deep learning convolutional neural networks on a public dataset. Results have shown that the proposed method can learn a salient feature representation and subsequently recognize the activities with an accuracy of 0.934 and perform comparably well to the convolutional neural networks.

关键词： Activity recognition denoising autoencoder Deep learning Feature learning

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DEVDAN: Deep evolving denoising autoencoder

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NEUROCOMPUTING 2020年 390卷 297-314页

作者： Ashfahani, Andri Pratama, Mahardhika Lughofer, Edwin Ong, Yew Soon Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Johannes Kepler Univ Linz Linz Austria

The denoising autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol. (C) 2019 Elsevier B.V. All rights reserved.

关键词： denoising autoencoder Data streams Incremental learning

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Multiple ECG segments denoising autoencoder model

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2023年第3期68卷 275-284页

作者： Samann, Fars Schanze, Thomas TH Mittelhessen THM Inst Biomed Tech IBMT FB Life Sci Engn LSE Wiesenstr 14 D-35390 Giessen Germany Univ Duhok Dept Biomed Engn Duhok 42001 Iraq

Objectives: denoising autoencoder (DAE) with a single hidden layer of neurons can recode a signal, i.e., converting the original signal into a noise-reduced signal. The DAE approach has shown a good performance in denoising bio-signals, like electrocardiograms (ECG). In this paper, we study the effect of correlated, uncorrelated and jittered datasets on the performance of the DAE ***: Vectors of multiple concatenated ECG segments of simultaneously recorded Einthoven recordings I, II, III are considered to establish the following dataset cases: (1) correlated, (2) uncorrelated, and (3) jittered. We consider our previous work in finding the optimal number of hidden neurons receiving the input signal with respect to signal quality and computational burden by applying Akaike's information criterion. To evaluate DAE, these datasets are corrupted with six types of noise, namely mix noise (MX), motion artifact noise (MA), electrode movement (EM), baseline wander (BW), Gaussian white noise (GWN) and high-frequency noise (HFN), to simulate real case scenario. Spectral analysis is used to study the effects of noise whose power spectrum may overlap with the power spectrum of the wanted signal on DAE ***: The simulation results show (a) that the number of hidden neurons to denoise multiple correlated ECG is much lower than for jittered signals, (b) QRS-complex based ECG alignment preferable, (c) noises with slightly overlapping power spectrum, like BW and HFN, can be easily removed with sufficient number of neurons, while the noise with completely overlapping spectrum, like GWN, requires a very low-dimensional and thus coarser reduction to recover the ***: The performance of DAE model in terms of signal-to-noise ratio improvement and the required number of hidden neurons can be improved by utilizing the correlation among simultaneous Einthoven I, II, III records.

关键词： Akaike's information criterion denoising autoencoder multiple ECG segments physical noises spectral analysis

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Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks

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ENERGY REPORTS 2021年 7卷 6354-6365页

作者： Jia, Xiongjie Han, Yang Li, Yanjun Sang, Yichen Zhang, Guolei Harbin Engn Univ Coll Power & Energy Engn Harbin Peoples R China

With the proportion of wind power in the grid increasing, the monitoring and maintenance of wind turbines is becoming more and more important for the reliability of the grid. In this study, a data-driven modelling framework based on deep convolutional neural networks is constructed for wind turbines condition monitoring (CM) and performance forecasting (PF). For CM, a robust denoising autoencoder (DAE) model is introduced to output the reconstruction error (RE) of raw signals. The RE is processed to a state indicator by exponentially weighted moving average (EWMA) and monitored on a control chart. For PF, two multi-steps ahead forecasting models are constructed for the forecasting of generator bearing and transformer temperature. To prevent overfitting caused by abundant features, the marginal effect analysis based on random forests is implemented to measure the importance of features. Besides, novel residual attention module (RAM) and training strategies are used improve their representation power of DAE and PF models. Experiments on a real wind turbine dataset prove the effectiveness of the proposed models and methods. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Wind turbine Condition monitoring Performance forecasting denoising autoencoder Residual attention module

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Trust-Aware Collaborative Filtering with a denoising autoencoder

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NEURAL PROCESSING LETTERS 2019年第2期49卷 835-849页

作者： Wang, Meiqi Wu, Zhiyuan Sun, Xiaoxin Feng, Guozhong Zhang, Bangzuo Northeast Normal Univ Sch Informat Sci & Technol Changchun 130117 Jilin Peoples R China

Collaborative filtering is one of the most successful and extensive methods used by recommender systems for predicting the preferences of users. However, traditional collaborative filtering only uses rating information to model the user, the data sparsity problem and the cold start problem will severely reduce the recommendation performance. To overcome these problems, we propose two neural network models to improve recommendations. The first one called TDAE uses a denoising autoencoder to integrate the ratings and the explicit trust relationships between users in the social networks in order to model the preferences of users more accurately. However, the explicit trust information is very sparse, which limits the performance of this model. Therefore, we propose a second method called TDAE++ for extracting the implicit trust relationships between users with similarity measures, where we employ both the explicit and implicit trust information together to improve the quality of recommendations. Finally, we inject the trust information into both the input and the hidden layer in order to fuse these two types of different information to learn more reliable semantic representations of users. Comprehensive experiments based on three popular data sets verify that our proposed models perform better than other state-of-the-art approaches in common recommendation tasks.

关键词： Collaborative filtering denoising autoencoder Social networks Trust information

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SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and denoising autoencoder

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第12期27卷 5779-5790页

作者： Chen, Guanyu Shi, Tianyi Xie, Baoxing Zhao, Zhicheng Meng, Zhu Huang, Yadong Dong, Jin Beijing Acad Blockchain & Edge Comp Beijing 100080 Peoples R China Beijing Univ Posts & Telecommun Beijing 100088 Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing 100124 Peoples R China Zhoushan Acad Marine Data Sci Zhoushan 316021 Peoples R China

Objective: Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. Methods: In this paper, an effective model named Swin denoising autoencoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. Results: SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. Conclusion: The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. Significance: SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

关键词： Electrocardiography Quality assessment Feature extraction Deep learning Data models Sensitivity Machine learning denoising autoencoder ECG quality assessment swin Transfromer transfer learning waveform component localization

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3-D BLE Indoor Localization Based on denoising autoencoder

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IEEE ACCESS 2017年 5卷 12751-12760页

作者： Xiao, Chao Yang, Daiqin Chen, Zhenzhong Tan, Guang Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China Chinese Acad Sci SIAT Shenzhen 518005 Peoples R China

Bluetooth low energy (BLE)-based indoor localization has attracted increasing interests for its low-cost, low-power consumption, and ubiquitous availability in mobile devices. In this paper, a novel denoising autoencoder-based BLE indoor localization (DABIL) method is proposed to provide high-performance 3-D positioning in large indoor places. A deep learning model, called denoising autoencoder, is adopted to extract robust fingerprint patterns from received signal strength indicator measurements, and a fingerprint database is constructed with reference locations in 3-D space, rather than traditional 2-D plane. Field experiments show that 3-D space fingerprinting can effectively increase positioning accuracy, and DABIL performs the best in terms of both horizontal accuracy and vertical accuracy, comparing with a traditional fingerprinting method and a deep learning-based method. Moreover, it can achieve stable performance with incomplete beacon measurements due to unpredictable BLE beacon lost.

关键词： Bluetooth low energy indoor localization fingerprinting denoising autoencoder

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