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检索条件"主题词=deep autoencoder"

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Outlier Resistant Unsupervised deep Architectures for Attributed Network Embedding 20

Outlier Resistant Unsupervised Deep Architectures for Attrib...

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13th Annual ACM International Conference on Web Search and Data Mining (WSDM)

作者： Bandyopadhyay, Sambaran Lokesh, N. Vivek, Saley Vishal Murty, M. N. IBM Res Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450368223

Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.

关键词： network representation learning community outliers adversarial learning deep autoencoder graph mining social networks

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deep Multi-view Subspace Clustering Network with Exclusive Constraint 40

Deep Multi-view Subspace Clustering Network with Exclusive C...

引用

40th Chinese Control Conference (CCC)

作者： Ma Rui Zhou Zhiping Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Engn Res Ctr Internet Things Technol Applicat Min Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9789881563804

Multi-view subspace clustering aims to find the inherent structure of data as much as possible by fusing complementary information of multiple views to achieve better clustering results. However, most of the traditional multi-view subspace clustering algorithms are only shallow clustering algorithms, which does not capture the deep information of the data well, and does not conduct in- depth research at the self- representation level of the data. To this end, this paper proposes a novel deep multi-view subspace clustering model that introduces exclusive constraints. A deep autoencoder is used to perform nonlinear low-dimensional subspace mapping for each view to learn the deep structure of the original data. To better retain multiple views' local structure and better reflect the complementarity, the exclusive constraints are introduced into the self-representation matrix which located in the middle layer of the deep autoencoder. The multi-view consensus self-representation matrix is used to capture the consistency information between the multi-view data. The update of autoencoder parameters and clustering parameters are iteratively optimized under the same learning framework to improve the clustering performance. Experiments on multi-view data sets prove that this method can better dig out the inherent complementary structure of multi-view data, which reflects the superiority of this method.

关键词： Multi-view clustering deep autoencoder Exclusive constraint Self-expression

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DIGITAL DATA FORGETTING: A Machine Learning Approach 2

DIGITAL DATA FORGETTING: A Machine Learning Approach

引用

2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Gunay, Melike Yildiz, Eyyup Nalcakan, Yagiz Asiroglu, Batuhan Zencirli, Ahmet Mete, Busra Rumeysa Ensari, Tolga Istanbul Kultur Univ Comp Engn Istanbul Turkey Erzincan Binali Yildirim Univ Comp Engn Erzincan Turkey Altinbas Univ Comp Engn Istanbul Turkey Istanbul Univ Cerrahpasa Comp Engn Istanbul Turkey

ISBN: (纸本)9781538641842

Digital transformation of the world goes very fast during last two decades. Today, data is power and very important. Firstly, magnetic tapes and then digital data storages have been used to collect all data. After this process, big data and its tool machine learning became very popular in both literature and industry. People use machine learning in order to obtain meaningful information from the big data. It brings valuable planning results. However, nowadays it is quite hard to collect and store all digital data to computers. This process is expensive and we will not have enough space to store data in the future. Therefore, we need and propose "Digital Data Forgetting" phrase with machine learning approach. With this digital / software solution, we will have more valuable data and will be able to erase the rest of them. We called this operation "Big Cleaning". In this article, we use a data set to get and extract more valuable data with principal component analysis (PCA), deep autoencoder and k-nearest neighbor machine learning methods in the experimental analysis section.

关键词： Digital Data Forgetting Machine Learning deep autoencoder Big Cleaning

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Accelerating Robot Reinforcement Learning with Accumulation of Knowledge

Accelerating Robot Reinforcement Learning with Accumulation ...

引用

30th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Loncarevic, Zvezdan Gams, Andrej Jozef Stefan Inst Jamova Cesta 39 Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Jamova Cesta 39 Ljubljana 1000 Slovenia

ISBN: (纸本)9783030752613;9783030752590;9783030752583

Reinforcement Learning (RL) can be applied in robotics to refine robot skills, but it may take several tens or even hundreds of attempts, which are typically just forgotten. In this paper, we show how all learning attempts, even those that do not fulfill the intended task, can be used to accelerate RL on the long-run. In a manner of non-continual lifelong learning we re-train a deep neural network that maps between the desired target/action outcome and the action in order to provide an initial estimate. Furthermore, for even faster learning, it is performed in reduced - latent space of a deep autoencoder. Robot throwing experiments clearly indicate, that the proposed methodology accelerates RL.

关键词： deep autoencoder Reinforcement learning Neural networks Robotic throwing

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Fast Data Driven Estimation of Cluster Number in Multiplex Images using Embedded Density Outliers

Fast Data Driven Estimation of Cluster Number in Multiplex I...

引用

IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Thomas, Spencer Angus Univ Surrey Dept Comp Sci Guildford Surrey England Natl Phys Lab NPL Data Sci Teddington Middx England

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

The usage of chemical imaging technologies is becoming a routine accompaniment to traditional methods in pathology. Significant technological advances have developed these next generation techniques to provide rich, spatially resolved, multidimensional chemical images. The rise of digital pathology has significantly enhanced the synergy of these imaging modalities with optical microscopy and immunohistochemistry, enhancing our understanding of the biological mechanisms and progression of diseases. Techniques such as imaging mass cytometry provide labelled multidimensional (multiplex) images of specific components used in conjunction with digital pathology techniques. These powerful techniques generate a wealth of high dimensional data that create significant challenges in data analysis. Unsupervised methods such as clustering are an attractive way to analyse these data, however, they require the selection of parameters such as the number of clusters. Here we propose a methodology to estimate the number of clusters in an automatic data-driven manner using a deep sparse autoencoder to embed the data into a lower dimensional space. We compute the density of regions in the embedded space, the majority of which are empty, enabling the high density regions (i.e. clusters) to be detected as outliers and provide an estimate for the number of clusters. This framework provides a fully unsupervised and data-driven method to analyse multidimensional data. In this work we demonstrate our method using 45 multiplex imaging mass cytometry datasets. Moreover, our model is trained using only one of the datasets and the learned embedding is applied to the remaining 44 images providing an efficient process for data analysis. Finally, we demonstrate the high computational efficiency of our method which is two orders of magnitude faster than estimating via computing the sum squared distances as a function of cluster number.

关键词： Clustering Data driven analysis multiplex imaging deep autoencoder transfer learning hyperspectral data

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Ensemble of DAEs for Fault Detection of Gas Turbine Engines 4

Ensemble of DAEs for Fault Detection of Gas Turbine Engines

引用

4th International Conference on System Reliability and Safety Engineering (SRSE)

作者： Ma, Shuai Wu, Yafeng Zheng, Hua Northwestern Polytech Univ Power & Energy Coll Xian Peoples R China

ISBN: (纸本)9781665473880

Aiming at the problem of limited fault-type samples, an ensemble of deep autoencoders (DAE) based fault detection approach for gas turbine engines was proposed. The proposed structure first transferred the measurement features into residual features through the physical model of gas turbine engines. Then a set of one-class classifiers based on DAEs and one-class support vector machines (OCSVM) were constructed as the base classifiers of the ensemble. The final prediction result was determined by majority voting. This structure only uses the flight data of the engine under health conditions and limited degraded conditions. The fault detection system was simulated using a dynamic model of a twin-shaft turbofan engine. The results show that the proposed approach can obtain more accurate and reliable fault detection results than the compared fault detection algorithms.

关键词： deep autoencoder one-class classification fault detection gas turbine engine ensemble learning

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GTDNN-Based Voice Conversion Using DAEs with Binary Distributed Hidden Units 11

GTDNN-Based Voice Conversion Using DAEs with Binary Distribu...

引用

11th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Ding, Yi-Yang Hu, Ya-Jun Ling, Zhen-Hua Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China

ISBN: (纸本)9781538656273

This paper proposes a method that adopts deep autoencoders with binary distributed hidden units (BDAE) as feature extractors in generatively trained DNNs (GTDNN) for voice conversion (VC). In this method, the source and target speakers are modeled separately by two BDAEs, and the extracted high-level features are used to train a Bernoulli bidirectional associated memory network (BBAM) for feature mapping. Then, the estimated model parameters are copied to construct a DNN for voice conversion. Compared with other neural network-based features extractors, such as deep belief networks (DBN) and conventional deep autoencoders (DAE), BDAEs can obtain a better balance between reconstruction error and the degree of binarization of hidden units. Our experimental results show that the GTDNN using BDAEs achieved better naturalness and similarity of converted speech than the ones using DBNs and conventional DAEs. Further experiments show the BDAE model for target speaker played a more important role than the one for source speaker.

关键词： voice conversion deep neural network deep autoencoder feature extraction

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Enhancing IoT Security: Federated Learning with GANs for Effective Attack Detection 20

Enhancing IoT Security: Federated Learning with GANs for Eff...

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20th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

作者： Saeed, Bakhtawar Arshad, Sobia Saeed, Sanay Muhammad Umar Azam, Muhammad Awais UET Taxila Comp Engn Dept Taxila Pakistan UET Taxila Natl Ctr Cyber Secur NCCS Deep Packet Inspect Lab DPI Taxila Pakistan Whitecliffe Sch Informat Technol Technol Innovat Res Grp Auckland New Zealand

ISBN: (纸本)9798350308266;9798350308259

The rapid proliferation of the Internet of Things (IoT) has given rise to security challenges, necessitating the real-time detection and mitigation of cyberattacks. Federated Learning (FL) is promising because it enables collaborative global model training in IoT for attack detection while maintaining data privacy. However, FL-enabled systems for IoT face challenges related to class imbalance, inconsistent data distributions, and increased training time. This paper presents a novel FL-enabled approach for IoT attack detection that effectively resolves class imbalance by utilizing Generative Adversarial Networks (GANs). The proposed approach also handles non-iid data distributions through the implementation of Vertical FL. Additionally, ordinal encoding techniques are employed to enhance detection accuracy. The effectiveness of the proposed approach is extensively evaluated on the ToN-IoT and N-BaIoT datasets, the DAEs and MLPs both deep learning (DL) algorithms consistently models achieve high precision, recall, and F1 score on ToN-IoT (MLPs: 99.39%, DAEs: 99.62%) and N-BaIoT datasets (DAEs: 93.66%, MLPs: 94.63%), demonstrating its capability in improving attack detection accuracy and mitigating the limitations of FL-enabled systems in IoT scenarios.

关键词： Federated learning IoT deep autoencoder Anomaly detection Multilayer perceptron

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Learning deep Embedding for Community Detection

Learning Deep Embedding for Community Detection

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International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Pan, Yu Yang, Jiupeng Wang, Shuaihui Zou, Junhua Hu, Guyu Pan, Zhisong Army Engn Univ Nanjing Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781728196190

Community detection is a classic and challenging network analysis task. Inspired by the similarity between autoencoder and modularity maximization model in terms of a low-dimensional approximation of the modularity matrix. In this paper, a novel community detection framework via deep autoencoder was proposed. Firstly, the modularity is fed into the autoencoder, which can obtain a non-linear deep representation for the network. In addition, node attribute similarity was utilized to construct pairwise constraints on nodes, and then a graph regularization is introduced into the framework. The extensive experimental evaluations on real-world networks and synthetic networks demonstrate that the proposed framework achieves superior performance compared with the state-of-the-art community detection algorithms.

关键词： community detection deep autoencoder modularity maximization model node attributes

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deep Anomaly Detection on Attributed Networks 19

Deep Anomaly Detection on Attributed Networks

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SIAM International Conference on Data Mining (SDM)

作者： Ding, Kaize Li, Jundong Bhanushali, Rohit Liu, Huan Arizona State Univ Comp Sci & Engn Tempe AZ 85281 USA

ISBN: (纸本)9781611975673

Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on attributed networks has attracted an increasing amount of research attention, with broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Most of the existing attempts, however, tackle the problem with shallow learning mechanisms by ego-network or community analysis, or through subspace selection. Undoubtedly, these models cannot fully address the computational challenges on attributed networks. For example, they often suffer from the network sparsity and data nonlinearity issues, and fail to capture the complex interactions between different information modalities, thus negatively impact the performance of anomaly detection. To tackle the aforementioned problems, in this paper, we study the anomaly detection problem on attributed networks by developing a novel deep model. In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN);and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data. The synergy between GCN and autoencoder enables us to spot anomalies by measuring the reconstruction errors of nodes from both the structure and the attribute perspectives. Extensive experiments on real-world attributed network datasets demonstrate the efficacy of our proposed algorithm.

关键词： Anomaly Detection Attributed Networks Graph Convolutional Network deep autoencoder

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