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Representation learning with collaborative autoencoder for personalized recommendation

EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115825-115825页

作者： Zhu, Yi Wu, Xindong Qiang, Jipeng Yuan, Yunhao Li, Yun Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing Peoples R China

In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful recommendation approaches, which refers to using the preferences of groups with similar interests to recommend information to other users. Recently, in addition to the traditional matrix factorization techniques, deep learning methods have been proposed to learn more abstract and higher-level representations for recommendation. However, most previous deep recommendation methods learn the higher level feature representations of users and items through an identical model structure, which ignores the different characteristics of the user-based and item-based data. In addition, the rating matrix is usually sparse which may result in a significant degradation of recommendation performance. To address these problems, we propose a representation learning method with Collaborative autoencoder for Personalized Recommendation (CAPR for short). In this method, user-based and item-based feature representations are learned by two different autoencoders for capturing different features of the data. Meanwhile, items' attributions are combined into the feature representations with semi-autoencoder for alleviating the sparsity problem. Extensive experimental results confirm the effectiveness of our proposed method compared to other state-of-the-art matrix factorization methods and deep recommendation methods.

关键词： autoencoder Personalized recommendation Semi-autoencoder Representation learning Collaborative filtering

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Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in autoencoder

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SENSORS 2021年第8期21卷 2708页

作者： de Castro-Cros, Marti Rosso, Stefano Bahilo, Edgar Velasco, Manel Angulo, Cecilio Univ Politecn Cataluna Intelligent Data Sci & Artificial Intelligence Re Dept Automat Control Campus Nord Barcelona 08034 Spain Siemens Energy SL Digihub Barcelona Barcelona 08940 Spain Siemens Energy SL Slottsvagen 2-6 S-61231 Finspang Sweden

Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

关键词： artificial intelligence autoencoder soft sensor condition assessment gas turbine

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Image noise reduction by denoising autoencoder 11

Image noise reduction by denoising autoencoder

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IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT) - IoT, Big Data and AI for a Safe & Secure World and Industry 4.0

作者： Yasenko, Lev Klyatchenko, Yaroslav Tarasenko-Klyatchenko, Oksana Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Dept Syst Programming & Specialized Comp Syst Kiev Ukraine

ISBN: (纸本)9781728199573

Neural networks are used in many tasks today. One of them is the images processing. autoencoder is very popular neural networks for such problems. Denoising autoencoder is an important autoencoder because some tasks we need a preprocessed image to get less noisy result. This research describes ways to analyze noisy images produced by a physically-based render engine and how to reduce that noise. The results showed that the algorithms are logarithmic.

关键词： render noise autoencoder denoise neural network

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Non-local Self-attentive autoencoder for Genetic Functionality Prediction 20

Non-local Self-attentive Autoencoder for Genetic Functionali...

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29th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Li, Yun Liu, Zhe Yao, Lina He, Zihuai Univ New South Wales Sydney NSW Australia Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781450368599

A big challenge existing in genetic functionality prediction is that genetic datasets comprise few samples but massive unclear structured features, i.e., 'large p, small N' problem. To tackle this problem, we propose Non-local Self-attentive autoencoder (NSAE) which applies attention-driven genetic variant modelling. The backbone attention layer captures long-range dependency relationship among cells (i.e., features) and thus allocates weights to construct attention maps based on cell significance. Utilizing attention maps, NSAE can effectively seize and leverage significant features in a non-local way from numerous cells. Our proposed NSAE outperforms the state-of-the-art algorithms on two genomics datasets from Roadmap projects. The visualization of the attention layer also validates NSAE's ability to highlight important features.

关键词： Neural prediction Non-local attention Genetic data autoencoder

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BLIND HYPERSPECTRAL UNMIXING USING DUAL BRANCH DEEP autoencoder WITH ORTHOGONAL SPARSE PRIOR

BLIND HYPERSPECTRAL UNMIXING USING DUAL BRANCH DEEP AUTOENCO...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dou, Zeyang Gao, Kun Zhang, Xiaodian Wang, Hong Wang, Junwei Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China

ISBN: (纸本)9781509066315

Blind hyperspectral unmixing has become an important task for hyperspectral applications. In this paper, we propose a dual branch autoencoder with a novel sparse prior to simultaneously extract endmembers and abundances from the raw HSI. The dual branch structure extends the linear mixing model by only modeling linear mixtures of the endmembers and treating the bilinear interactions as error. In this way, the proposed model doesn't require the assumptions of explicit forms of bilinear interactions. The proposed sparse prior, named as orthogonal sparse prior, is based on the key observation that the abundance vector of one pixel is very sparse, there are often no more than two non-zero elements. Different from the conventional norm-based sparse prior which assumes the abundance maps are independent, the orthogonal sparse prior explores the orthogonality between the abundance maps. Extensive experiments on two real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods, with up to 50% improvements.

关键词： Hyperspectral unmixing Sparse prior autoencoder

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An autoencoder-embedded Evolutionary Optimization Framework for High-dimensional Problems

An Autoencoder-embedded Evolutionary Optimization Framework ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Cui, Meiji Li, Li Zhou, MengChu Tongji Univ Dept Elect & Informat Engn Shanghai 201804 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781728185262

Many ever-increasingly complex engineering optimization problems fall into the class of High-dimensional Expensive Problems (HEPs), where fitness evaluations are very time-consuming. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. In this paper, an autoencoder-embedded Evolutionary Optimization (AEO) framework is proposed for the first time. As an efficient dimension reduction tool, an autoencoder is used to compress high-dimensional landscape to informative low-dimensional space. The search operation in this low-dimensional space can facilitate the population converge towards the optima more efficiently. To balance the exploration and exploitation ability during optimization, two sub-populations coevolve in a distributed fashion, where one is assisted by an autoencoder and the other undergoes a regular evolutionary process. The information between these two sub-populations are dynamically exchanged. The proposed algorithm is validated by testing several 200 dimensional benchmark functions. Compared with the state-of-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems.

关键词： High-dimensional expensive problems (HEPs) autoencoder artificial intelligence evolutionary algorithms dimension reduction machine learning

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Least Square Adversarial autoencoder 12

Least Square Adversarial Autoencoder

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12th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Sinaga, Marshal Anjona Stefanus, Lim Yohanes Univ Indonesia Fac Comp Sci Depok 15424 Indonesia

ISBN: (纸本)9781728192796

This research introduces least square adversarial autoencoder (LSAA)-an autoencoder that is able to reconstruct data and also generate data that has characteristics similar to data distribution from the prior distribution LSAA uses least square generative adversarial network loss function on its discriminator. LSAA minimizes Pearson ? 2 divergence between the latent variable distribution and the prior distribution. In this research, a Python program is developed to model LSAA by utilizing MNIST data set and FashionMNIST data set. The program is implemented using PyTarch. All of the programming activities are carried out in the cloud environment provided by the Tokopedia-Universitas Indonesia AI Center, using DGR-1 (GPU Tesla V100) as its computing resource. The experimental results show that the mean squared error of LSAA for MNIST data set and FasbionMNIST data set are 0.0080 and 0.0099, respectively. Furthermore, the Frechet Inception Distance score of LSAA for MNIST data set and FashionMNIST data set are 11.1280 and 27.5737, respectively. These results indicate that the least square adversarial autoencoder is able to reconstruct the image properly and also able to generate images similar to the training samples.

关键词： artificial neural network autoencoder least square generative adversarial network regularization generative model

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Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks

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CHAOS SOLITONS & FRACTALS 2021年 144卷 110714-110714页

作者： Togacar, Mesut Comert, Zafer Ergen, Burhan Firat Univ Dept Comp Technol Elazig Turkey Samsun Univ Fac Engn Dept Software Engn Samsun Turkey Firat Univ Fac Engn Dept Comp Engn Elazig Turkey

Melanocytes are skin cells that give color to the skin and form melanin color pigments. The unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis and proper treatment of skin cancer are so important. In this scope, a novel model that relies upon the autoencoder, spiking, and convolutional neural networks is proposed to ensure a useful decision support tool in this study. The experiments were carried out on an open-access dataset called the ISIC skin cancer consisting of 1800 being and 1497 malignant tumor images. In the proposed approach, the dataset is reconstructed using the autoencoder model. The original dataset and structured dataset were trained and classified by the MobileNetV2 model that consists of residual blocks, and the spiking networks. The classification success rate of the study was 95.27%. As a result, it was seen that the autoencoder model and spiking networks contributed to enhancing the performance of the MobileNetV2 model. Thanks to the proposed model, a novel fully automated decision support tool with high sensitivity was ensured for skin cancer detection. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Biomedical signal processing Decision support Spiking neural network autoencoder MobileNet Skin cancer

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System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets

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APPLIED ENERGY 2021年 301卷 117458-117458页

作者： Hong, Yejin Yoon, Sungmin Kim, Yong-Shik Jang, Hyangin Incheon Natf Univ Div Architecture & Urban Design Incheon 22012 South Korea Incheon Natl Univ Inst Urban Sci Incheon 22012 South Korea Mirae Environm Plan Architects Inst Green Bldg & New Technol Seoul 01905 South Korea

Sensing networks and their environments are essential in intelligent building systems because of their increasing dependency on operational data. Virtual sensing technology has been applied in building energy systems to provide the more reliable and informative sensing environments. However, conventional virtual sensors still have structural and practical limitations under the physical sensor absences and limited datasets. Existing virtual sensors are separately developed by modeling multiple input variables and a single target (Xs to 1'), which is the variable-level virtual sensor (VLVS);therefore, these virtual sensors cannot benefit by either using their target variable (1') or by considering other virtual sensors when developing the models. This can result in insufficient accuracy, particularly in the limited sensors. Herein, to overcome these limitations, a novel virtual sensing framework, system-level virtual sensing (SLVS), is proposed for building energy systems using an autoencoder. Two strategies are also proposed. The autoencoder-based SLVS with the two strategies was applied in a real operational district heating system. The first strategy showed an improved accuracy using a new assistance virtual sensor, which is derived by additional information and knowledge regarding system design, control, and devices. It could also overcome the training data dependency in the limited datasets. The second strategy provided a replacement function for the SLVS specialized for backup and a calibration effect for the existing VLVS. Thus, the results showed that the suggested SLVS can achieve multifunctional high-accuracy virtual sensing;the accuracies of 99.89%, 99.68%, and 97.91% were shown respectively for temperatures, pressures, and control signals.

关键词： System-level virtual sensing Virtual sensors autoencoder District heating system Intelligent building energy systems

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Improving Image autoencoder Embeddings with Perceptual Loss

Improving Image Autoencoder Embeddings with Perceptual Loss

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pihlgren, Gustav Grund Sandin, Fredrik Liwicki, Marcus Lulea Univ Technol EISLAB Machine Learning Lulea Sweden Lulea Univ Technol EISLAB Elect Syst Lulea Sweden

ISBN: (纸本)9781728169262

autoencoders are commonly trained using element-wise loss. However, element-wise loss disregards high-level structures in the image which can lead to embeddings that disregard them as well. A recent improvement to autoencoders that helps alleviate this problem is the use of perceptual loss. This work investigates perceptual loss from the perspective of encoder embeddings themselves. autoencoders are trained to embed images from three different computer vision datasets using perceptual loss based on a pretrained model as well as pixel-wise loss. A host of different predictors are trained to perform object positioning and classification on the datasets given the embedded images as input. The two kinds of losses are evaluated by comparing how the predictors performed with embeddings from the differently trained autoencoders. The results show that, in the image domain, the embeddings generated by autoencoders trained with perceptual loss enable more accurate predictions than those trained with element-wise loss. Furthermore, the results show that, on the task of object positioning of a smallscale feature, perceptual loss can improve the results by a factor 10. The experimental setup is available online.(1)

关键词： autoencoder Perceptual Loss Image Processing Image Classification Object Positioning Embeddings

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