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An autoencoder-based deep learning model for solving the sparsity issues of Multi-Criteria Recommender System

Procedia Computer Science 2024年 235卷 414-425页

作者： Ishwari Singh Rajput Anand Shanker Tewari Arvind Kumar Tiwari Department of CSE National Institute of Technology Patna Patna 800005 India School of Computing Graphic Era Hill University Haldwani 263139 India Kamla Nehru Institute of Technology Sultanpur 228118 India

In recent times, recommender systems have acquired significant popularity as a solution to the issue of information overload. These systems offer personalised recommendations to users. The superiority of multi-criteria recommender systems over their single-criterion counterparts has been demonstrated, as the former are able to provide more precise recommendations by taking into account multiple dimensions of user preferences when rating items. The prevalent recommendation technique, matrix factorization of collaborative filtering, is hindered by the data sparsity problem of the user-item matrix. On the other hand, it is noteworthy that deep learning techniques have demonstrated significant potential in various research domains, including but not limited to image processing, pattern recognition, computer vision, and natural language processing. In recent times, there has been a surge in the utilisation of deep learning techniques in recommender systems, yielding promising outcomes. This study presents a novel approach to multi-criteria recommender systems through the utilisation of deep learning algorithms to mitigate the data sparsity issue. Specifically, deep autoencoders are utilised to uncover complex, non-linear, and latent relationships between users' multi-criteria preferences followed by matrix factorization technique, ultimately leading to more precise recommendations. The proposed model is evaluated by conducting the experiments on the multi-criteria dataset of Yahoo! Movies. According to the outcomes, the proposed approach outperforms the state of the art recommendation methods by generating more accurate and personalized recommendations. Also, it reduces the data sparsity up to 11% from the multi-criteria dataset.

关键词： Recommender System Collaborative filtering Data Sparsity Multi-criteria autoencoder

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Geometrically shaped four-dimensional modulation formats for 5-7 bits/symbol designed by autoencoder

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IEICE COMMUNICATIONS EXPRESS 2024年第4期13卷 106-109页

作者： Naka, Akira Komatsu, Mamoru Ibaraki Univ Dept Elect & Elect Syst Engn 4-12-1 Naka Narusawa Hitachi Ibaraki 3168511 Japan

We design geometrically shaped four-dimensional (4D) modulation formats for 5-7 bits/4D symbol with an autoencoder, which is one of the deep learning applications, using pre-training with 4D formats based on2-ary amplitude 8-ary phase-shift keying. The numerical evaluation shows that the obtained 4D modulation formats have 0.5-0.7dB signal-to-noise sensitivity gains in terms of normalized generalized mutual information(NGMI) as well as bit error ratio (BER) performances. Furthermore, weshow the NGMI and BER performances of these 4D modulation formatsare almost equivalent to those of probabilistic amplitude shaping with enumerative sphere shaping with a block length of 20.

关键词： geometric shaping 4D modulation autoencoder enumerativesphere shaping 2-ary amplitude 8-ary phase-shift keying

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A restorable autoencoder as a method for dimensionality reduction

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JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2021年第4期78卷 315-327页

作者： Jeong, Yeongcheol Kim, Sunhee Lee, Chang-Yong Kongju Natl Univ Dept Ind & Syst Engn Cheonan 31080 South Korea

In this paper, we propose a restorable autoencoder model as a non-linear method for reducing dimensionality. While non-linear methods can reduce the dimensionality of data more effectively than linear methods, they are, in general, not able to restore the original data from the dimensionality-reduced result. This is because non-linear methods provide a non-linear relationship between the original data and their dimensionality-reduced result. With the advantages of both linear and non-linear methods, the proposed model not only maintains an effective dimensionality reduction but also provides an observation-wise linear relationship with which the original data can be restored from the dimensionality-reduced result. We assessed the effectiveness of the proposed model and compared it with the linear method of principal component analysis and the non-linear methods of typical autoencoders using MNIST and Fashion-MNIST data sets. We demonstrated that the proposed model was more effective than or comparable to the compared methods in terms of the loss function and the reconstruction of input images. We also showed that the lower-dimensional projection obtained by the proposed model produced better or comparable classification results than that by the compared methods.

关键词： autoencoder Dimensionality reduction Principal component analysis Linear transformation

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An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and autoencoder

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SENSORS 2023年第8期23卷 3908页

作者： Guo, Jinhua Wang, Jiaquan Xiao, Fang Zhou, Xiao Liu, Yongsheng Ma, Qiming Shanghai Univ Elect Power Sch Math & Phys Shanghai 201306 Peoples R China Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China Shanghai Univ Elect Power Inst Solar Energy Shanghai 200090 Peoples R China

Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time. The storage and transmission of the obtained data is a crucial link, and a good compression method can improve the efficiency of this process. In this paper, a lightning convolutional stack autoencoder (LCSAE) model for compressing LEMP data was designed, which converts the data into low-dimensional feature vectors through the encoder part and reconstructs the waveform through the decoder part. Finally, we investigated the compression performance of the LCSAE model for LEMP waveform data under different compression ratios. The results show that the compression performance is positively correlated with the minimum feature of the neural network extraction model. When the compressed minimum feature is 64, the average coefficient of determination R-2 of the reconstructed waveform and the original waveform can reach 96.7%. It can effectively solve the problem regarding the compression of LEMP signals collected by the lightning sensor and improve the efficiency of remote data transmission.

关键词： lightning deep learning feature compression autoencoder convolutional neural network

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A comprehensive survey on design and application of autoencoder in deep learning

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APPLIED SOFT COMPUTING 2023年 138卷

作者： Li, Pengzhi Pei, Yan Li, Jianqiang Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan Univ Aizu Comp Sci Div Aizu Wakamatsu Fukushima 9658580 Japan Beijing Univ Technol Sch Software Engn Beijing 100124 Peoples R China

autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality reduction method. With the development of deep learning technology, autoencoder has attracted the attention of many scholars. Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles. The related autoencoder models are comprehensively analyzed and discussed. This paper introduces the application progress of autoencoders in different fields, such as image classification and natural language processing, etc. Finally, the shortcomings of the current autoencoder algorithm are summarized, and prospected for its future development directions are addressed. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Deep learning autoencoder Unsupervised learning Feature extraction autoencoder application

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FCAE-AD: Full Convolutional autoencoder Based on Attention Gate for Hyperspectral Anomaly Detection

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REMOTE SENSING 2023年第17期15卷 4263-4263页

作者： Wang, Xianghai Wang, Yihan Mu, Zhenhua Wang, Ming Liaoning Normal Univ Sch Geog Sci Dalian 116029 Peoples R China Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian 116029 Peoples R China

Recently, the methods based on the autoencoder reconstruction background have been applied to the area of hyperspectral image (HSI) anomaly detection (HSI-AD). However, the encoding mechanism of the autoencoder (AE) makes it possible to treat the anomaly and the background indistinguishably during reconstruction, which can result in a small number of anomalous pixels still being included in the acquired reconstruction background. In addition, the problem of redundant information in HSIs also exists in reconstruction errors. To this end, a fully convolutional AE hyperspectral anomaly detection (AD) network with an attention gate (AG) connection is proposed. First, the low-dimensional feature map as a product of the encoder and the fine feature map as a product of the corresponding decoding stage are simultaneously input into the AG module. The network context information is used to suppress the irrelevant regions in the input image and obtain the significant feature map. Then, the features from the AG and the deep features from upsampling are efficiently combined in the decoder stage based on the skip connection to gradually estimate the reconstructed background image. Finally, post-processing optimization based on guided filtering (GF) is carried out on the reconstruction error to eliminate the wrong anomalous pixels in the reconstruction error image and amplify the contrast between the anomaly and the background.

关键词： hyperspectral image anomaly detection autoencoder guided filtering attention gate

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A speed normalized autoencoder for rotating machinery fault detection under varying speed conditions

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期189卷

作者： Rao, Meng Zuo, Ming J. Tian, Zhigang Univ Alberta Dept Mech Engn Edmonton AB T6G 1H9 Canada Qingdao Mingserve Technol Ltd Qingdao 266000 Shandong Peoples R China Qingdao Int Academician Pk Res Inst Qingdao 266000 Shandong Peoples R China

Rotating machinery often operates under varying speed conditions. Fault detection is necessary to prevent sudden failures and enable condition-based maintenance. Existing autoencoder-based (AE-based) fault detection methods did not address the effects of speed variations, and thus leave room for improvement at varying speed conditions. This paper proposes a new deep learning model named speed normalized autoencoder (SN-AE). The SN-AE consists of a speed normalization (SN) branch and an AE branch. The SN branch takes the speed signal as the input and automatically learns an SN function which normalizes the vibration signal to remove the effects of speed variations. Thereafter, the normalized vibration signal is inputted to the AE branch for fault detection. Case studies were conducted to detect incipient faults of three typical rotating machines including a planetary gearbox, a fixed-shaft gearbox and a rolling element bearing under varying speed conditions. Results have shown that the proposed SN-AE successfully removes the effects of speed variations and achieves significantly better detection performances than existing AE-based fault detection methods.

关键词： Rotating machinery Fault detection Varying speed condition autoencoder Speed normalization

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Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-autoencoder

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 213卷

作者： Zhang, Kai Han, Shuqing Wu, Jianzhai Cheng, Guodong Wang, Yali Wu, Saisai Liu, Jifang Chinese Acad Agr Sci Agr Informat Inst Beijing 100081 Peoples R China Minist Agr & Rural Affairs Key Lab Agr Blockchain Applicat Beijing 100081 Peoples R China

Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learningbased gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-ofview limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

关键词： Early lameness detection in dairy cows Gait reconstruction Long short-term memory autoencoder Time series anomaly detection

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Anomaly detection for multivariate times series through the multi-scale convolutional recurrent variational autoencoder

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Xie, Tianming Xu, Qifa Jiang, Cuixia Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis making Hefei Peoples R China

To realize the anomaly detection for industrial multi-sensor data, we develop a novel multi-scale convolutional recurrent variational autoencoder (MSCRVAE) model. It is a hybrid of convolutional autoencoder and convolutional long short-term memory with variational autoencoder (ConvLSTM-VAE). The ConvLSTM-VAE part helps the MSCRVAE model not only capture the spatial and temporal dependence of data but also learn robust high-level representations by estimating the distributions of latent variables. Moreover, a typical loss function that combines the characteristics of both autoencoder and variational autoencoder is designed for the MSCRVAE model. The experimental results on three public data illustrate the superiority of the MSCRVAE model in anomaly detection with the best average F1-score up to 0.90. The interquartile range of the boxplots on the public data also proves the robustness of the MSCRVAE model. We also illustrate the role of the two components in the loss function by exploring their changes during training. What is more, on the private data, the MSCRVAE model also performs well and the residual matrices provide reasonable interpretations to the anomaly detection results.

关键词： Anomaly detection Multi-sensor data Multivariate time series Inner correlation Variational autoencoder Multi-scale convolutional recurrent variational autoencoder

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ECG-NET: A deep LSTM autoencoder for detecting anomalous ECG

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期124卷

作者： Roy, Moumita Majumder, Sukanta Halder, Anindya Biswas, Utpal Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 West Bengal India Brainware Univ Dept Computat Sci Kolkata 700125 West Bengal India Cotton Univ Dept Comp Sci & Informat Technol Gauhati 781001 India North Eastern Hill Univ Sch Technol Dept Comp Applicat Shillong 794002 Meghalaya India

The electrocardiogram (ECG) is a standard test to monitor the activity of the heart. Many cardiac abnormalities are manifested in the ECG including arrhythmia that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal heart beats, and their correct classification based on ECG morphology. This paper proposes a novel and robust approach for representation learning of ECG sequences using a LSTM autoencoder for anomaly *** encoder part encodes the ECG signal into a lower dimensional latent space representation and decoder part then tries to reconstruct the specified ECG signal. The model is trained only on normal (non-anomalous) ECG signals. Then reconstruction loss of test ECG signals are calculated. Next a reconstruction loss threshold value is determined from the frequency distribution of the reconstruction losses so that from the reconstruction loss value above a certain threshold is determined as anomaly, otherwise it will be treated as normal. Determination of threshold is done using manual and Kapur's automated thresholding *** aforementioned model has been applied on publicly available ECG5000 dataset. From the experimental results it is observed that the proposed model achieved more than 98% accuracy having precision, recall and F1 values more than 0.94, 0.97, 0.96 respectively. The performance of the proposed method is also found to be superior in most of the cases as compared to the results of seven other recent counter-part methods reported in the literature.

关键词： ECG arrhythmia Deep learning LSTM autoencoder Thresholding Kapur's thresholding

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