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Clustering With Orthogonal autoencoder

IEEE ACCESS 2019年 7卷 62421-62432页

作者： Wang, Wei Yang, Dan Chen, Feiyu Pang, Yunsheng Huang, Sheng Ge, Yongxin Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400030 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 400030 Peoples R China

Recently, clustering algorithms based on deep autoencoder attract lots of attention due to their excellent clustering performance. On the other hand, the success of PCA-Kmeans and spectral clustering corroborates that the orthogonality of embedding is beneficial to increase the clustering accuracy. In this paper, we propose a novel dimensional reduction model, called Orthogonal autoencoder (OAE), which encourages the orthogonality of the learned embedding. Furthermore, we propose a joint deep Clustering framework based on Orthogonal autoencoder (COAE), and this new framework is capable of extracting the latent embedding and predicting the clustering assignment simultaneously. The COAE stacks a fully connected clustering layer on top of the OAE, where the activation function of the clustering layer is the multinomial logistic regression function. The loss function of the COAE contains two terms: the reconstruction loss and the clustering-oriented loss. The first one is a data-dependent term in order to prevent overfitting. The other one is the cross entropy between the predicted assignment and the auxiliary target distribution. The network parameters of the COAE can be effectively updated by the mini-batch stochastic gradient descent algorithm and the back-propagation approach. The experiments on benchmark datasets empirically demonstrate that the COAE can achieve superior or competitive clustering performance as state-of-the-art deep clustering frameworks. The implementation of our algorithm is available at https://***/WangDavey/COAE

关键词： Deep clustering autoencoder orthogonality

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Supervised feature learning by adversarial autoencoder approach for object classification in dual X-ray image of luggage

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第5期31卷 1101-1112页

作者： Chouai, Mohamed Merah, Mostefa Sancho-Gomez, Jose-Luis Mimi, Malika Mostaganem Univ Dept Elect Engn Signals & Syst Lab Site 1Route Belahcel Mostaganem 27000 Algeria Univ Politecn Cartagena Informat & Commun Technol Dept TDAM Res Grp Data Proc & Machine Learning Plaza Hosp 1 Murcia 30202 Spain

X-ray inspection by control officers is not always consistent when inspecting baggage since this task are monotonous, tedious and tiring for human inspectors. Thus, a semi-automatic inspection makes sense as a solution in this case. In this perspective, the study presents a novel feature learning model for object classification in luggage dual X-ray images in order to detect explosives objects and firearms. We propose to use supervised feature learning by autoencoders approach. Object detection is performed by a modified YOLOv3 to detect all the presented objects without classification. The features learning is carried out by labeled adversarial autoencoders. The classification is performed by a support vector machine to classify a new object as explosive, firearms or non-threatening objects. To show the superiority of our proposed system, a comparative analysis was carried out to several methods of deep learning. The results indicate that the proposed system leads to efficient objects classification in complex environments, achieving an accuracy of 98.00% and 96.50% in detection of firearms and explosive objects respectively.

关键词： Transport security Baggage control Object detection YOLOv3 Features learning autoencoder

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Deep Learning Enabled autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

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Computers, Materials & Continua 2021年第7期68卷 487-503页

作者： Thavavel Vaiyapuri College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever ***,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate ***,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of *** of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality *** by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF *** proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in ***,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item ***,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP *** conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation *** experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation *** analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rat

关键词： Neural collaborative filtering cold-start problem data sparsity multilayer perception generalized matrix factorization autoencoder deep learning ensemble learning top-K recommendations

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Learning Stretch-Shrink Latent Representations With autoencoder and K-Means for Software Defect Prediction

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IEEE ACCESS 2022年 10卷 117827-117835页

作者： Phan, Viet Anh Le Quy Don Tech Univ Fac Informat Technol Dept Informat Secur Hanoi 100000 Vietnam

Detecting defective source code to localize and fix bugs is important to reduce software development efforts. Although deep learning models have made a breakthrough in this field, many issues have not been resolved, such as labeled data shortage and the small size of defective elements. Given two similar programs that differ from each other by an operator or statement, one may be clean while the other may be defective. To address these issues, this study proposes a new deep learning model to facilitate the learning of distinguishing features. The model comprises of three main components: 1) a convolutional neural network-based classifier, 2) an autoencoder, and 3) a k-means cluster. In our model, the autoencoder assists the classifier in generating program latent representations. The k-means cluster provides penalty functions to increase the distinguishability among latent representations. We evaluated the effectiveness of the model according to performance metrics and latent representation quality. The experimental results on the four defect prediction datasets show that the proposed model outperforms the baselines thanks to the generation of sophisticated features.

关键词： autoencoder k-means convolutional neural networks latent representations

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Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender system

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 42125页

作者： Behara, Gopal Yannam, V. Ramanjaneyulu Nayyar, Anand Bagal, Dilip Kumar Govt Coll Engn Kalahandi Dept Comp Sci & Engn Bhawanipatna 766003 Orissa India Natl Inst Technol Dept Comp Sci & Engn Rourkela 569008 Orissa India Duy Tan Univ Fac Informat Technol Grad Sch Da Nang 550000 Vietnam Govt Coll Engn Kalahandi Dept Mech Engn Bhawanipatna 766003 Orissa India

Nowadays, Deep learning (DL) techniques have been proven successful as learning techniques in various research fields ranging from computer vision to social networks. The approach of DL is flourishing in the field of recommender systems (RS). Researchers have deployed metadata or auxiliary information using DL approaches in diverse applications in the last decade to achieve better recommendation accuracy. Thus, the metadata plays a vital role in obtaining a better user-item interaction. At the same time, existing techniques are based on fixed user and item factors. Therefore, the model does not correctly identify actual latent factors representation, resulting in a high prediction error. To handle this problem, a user metadata embedding using a deep autoencoder RS model called "Metadata Embedding Deep autoencoder (MEDAE)" based collaborative filtering is proposed. MEDAE model takes embeds user metadata such as demographics along with the rating data. The MEDAE model consists of an embedding layer, Encoder, and Decoder. The embedding layer generates embedding or latent features of the users, items, and metadata;Encoder receives concatenated features of the user, item, and metadata, then encodes the inputs and passes them to the decoder;and the decoder reconstructs the output. To test the effectiveness of proposed model Root Mean Squared Error and Mean Absolute Error measures are used. Different architectures (like Big-Small-Big (BSB) (5), BSB (3), Small-Big-Small (3), and SBS (5)) of the MEDAE model are evaluated on MovieLens datasets along with different parameters such as activation functions (ELU and SELU) and regularization and results concluded that the MEDAE with SBS (3) and ELU + SELU component improves 4% of RMSE and 2% MAE over the baseline methods.

关键词： Collaborative Filtering Recommender System Deep Learning Metadata autoencoder

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Hyperspectral Unmixing Using a Neural Network autoencoder

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IEEE ACCESS 2018年 6卷 25646-25656页

作者： Palsson, Burkni Sigurdsson, Jakob Sveinsson, Johannes R. Ulfarsson, Magnus O. Univ Iceland Fac Elect & Comp Engn IS-107 Reykjavik Iceland

In this paper, we present a deep learning based method for blind hyperspectral unmixing in the form of a neural network autoencoder. We show that the linear mixture model implicitly puts certain architectural constraints on the network, and it effectively performs blind hyperspectral unmixing. Several different architectural configurations of both shallow and deep encoders are evaluated. Also, deep encoders are tested using different activation functions. Furthermore, we investigate the performance of the method using three different objective functions. The proposed method is compared to other benchmark methods using real data and previously established ground truths of several common data sets. Experiments show that the proposed method compares favorably to other commonly used hyperspectral unmixing methods and exhibits robustness to noise. This is especially true when using spectral angle distance as the network's objective function. Finally, results indicate that a deeper and a more sophisticated encoder does not necessarily give better results.

关键词： Hyperspectral unmixing autoencoder deep learning neural network spectral angle distance endmember extraction

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User-centric hybrid semi-autoencoder recommendation system

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第16期81卷 23091-23104页

作者： Tewari, Anand Shanker Parhi, Ityendu Al-Turjman, Fadi Abhishek, Kumar Ghalib, Muhummad Rukunuddin Shankar, Achyut NIT Patna CSE Dept Patna Bihar India Near East Univ Dept Artificial Intelligence Engn Res Ctr AI & IoT Mersin 10 Nicosia Turkey Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Amity Univ Amity Sch Engn & Technol Dept CSE Noida India

Recommendation System is one of such solutions to overcome information overload issues and to identify products most relevant to users and provide suggestions to users for items they might be interested in consuming or elements matching their needs. The significant challenge of several recommendation approaches is that they suggested a huge number of things to the target user. But the exciting items, according to the target user, are seen at the bottom of the recommended list. The proposed approach has improved the quality of recommendations by implementing some of the unique features in the new framework of auto encoder called semi-autoencoder, which contains the rating information as well as some additional information of users. autoencoder is widely used in the recommender system because it gives the best result for feature extraction, dimensionality reduction, regeneration of data, and a better understanding of the user's characteristics. The experimental results are compared with some established popular methods using precision, recall, and F-measure evaluation measures. Users generally don't want to see lots of suggestions. With its six building blocks, the proposed approach gives better performance for the top 10 recommendations compared to other well-known methods.

关键词： Recommendation system autoencoder E-commerce Dimensionality reduction Collaborative filtering

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Deep Manifold Preserving autoencoder for Classifying Breast Cancer Histopathological Images

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第1期17卷 91-101页

作者： Feng, Yangqin Zhang, Lei Mo, Juan Sichuan Univ Machine Intelligence Lab Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

Classifying breast cancer histopathological images automatically is an important task in computer assisted pathology analysis. However, extracting informative and non-redundant features for histopathological image classification is challenging due to the appearance variability caused by the heterogeneity of the disease, the tissue preparation, and staining processes. In this paper, we propose a new feature extractor, called deep manifold preserving autoencoder, to learn discriminative features from unlabeled data. Then, we integrate the proposed feature extractor with a softmax classifier to classify breast cancer histopathology images. Specifically, it learns hierarchal features from unlabeled image patches by minimizing the distance between its input and output, and simultaneously preserving the geometric structure of the whole input data set. After the unsupervised training, we connect the encoder layers of the trained deep manifold preserving autoencoder with a softmax classifier to construct a cascade model and fine-tune this deep neural network with labeled training data. The proposed method learns discriminative features by preserving the structure of the input datasets from the manifold learning view and minimizing reconstruction error from the deep learning view from a large amount of unlabeled data. Extensive experiments on the public breast cancer dataset (BreaKHis) demonstrate the effectiveness of the proposed method.

关键词： Breast cancer Manifolds Feature extraction Neural networks Training Computer architecture Histopathological image classification breast cancer diagnose manifold learning autoencoder deep neural networks

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An autoencoder Based Background Subtraction for Public Surveillance

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2021年第10期E104A卷 1445-1449页

作者： Li, Yue Yu, Xiaosheng Cao, Haijun Xu, Ming Northeastern Univ Sch Humanities & Law Shenyang 110819 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.

关键词： surveillance image background image autoencoder multi-scale features foreground detection

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autoencoder Feature Residuals for Network Intrusion Detection: Unsupervised Pre-training for Improved Performance 21

Autoencoder Feature Residuals for Network Intrusion Detectio...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Lewandowski, Brian Paffenroth, Randy Worcester Polytech Inst Comp Sci Worcester MA 01609 USA Worcester Polytech Inst Data Sci Math Sci & Comp Sci Worcester MA 01609 USA

ISBN: (纸本)9781665462839

Network intrusion detection is a constantly evolving field as researchers and practitioners work towards keeping up with novel attacks and growing amounts of network data. To aid in this challenge researchers have been exploring the use of deep learning techniques such as neural networks in order to detect zero-day attacks and reduce the amount of manual analysis required when a network intrusion detection system alert is generated. Herein we use an unsupervised pre-training step in order to take advantage of autoencoder feature residuals. We show that autoencoder feature residuals can be used in place of or in addition to an original feature set as input to a neural network classifier to improve classification performance. Often in such problems, experts perform feature engineering to optimize classification performance. However, such data manipulation is expensive and time consuming. Our novel approach provides a path that can alleviate the need for manual feature extraction while "doing no harm". That is, if the provided features are in some sense optimal, then our methodology will not degrade the classification performance. However, if the provided features are inefficient, then we demonstrate that our methodology can substantially improve classification performance on a broad range of benchmark cybersecurity datasets. Another practical side effect of using autoencoder feature residuals comes to light by analyzing the potential data compression benefits they provide.

关键词： autoencoder network intrusion detection deep learning

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