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Semi-Supervised Manifold Alignment Using Parallel deep autoencoders

ALGORITHMS 2019年第9期12卷

作者： Aziz, Fayeem Wong, Aaron S. W. Chalup, Stephan Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia 4Tel Pty Ltd Warabrook NSW 2304 Australia

The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conducts a series of experiments using a protein-folding benchmark dataset and a suite of new datasets generated by simulating double-pendulum dynamics with underlying manifolds of dimensions 2, 3 and 4. The dimensionality and topological complexity of these latent manifolds are above those occurring in most previous studies. Our experimental results demonstrate that the parallel deep autoencoder performs in most cases better than the tested traditional methods of semi-supervised manifold alignment. We also show that the parallel deep autoencoder can process datasets of different input domains by aligning the manifolds extracted from kinematics parameters with those obtained from corresponding image data.

关键词： deep autoencoder dimensionality reduction manifold learning 3-manifold machine learning manifold alignment autoencoder deep neural network deep learning double pendulum

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Unsupervised environmental operating condition compensation strategies in a guided ultrasonic wave monitoring system: evaluation and comparison

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2024年第4期14卷 861-884页

作者： Yon, Kong Chen Bakhary, Norhisham Padil, Khairul Hazman Shamsudin, Mohd Fairuz Univ Teknol Malaysia Fac Civil Engn Utm Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Mech Engn Utm Skudai 81310 Johor Malaysia Univ Teknol Malaysia Inst Noise & Vibrat City CampusJalan Semarak Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Inst Smart Infrastruct & Innovat Construct Forens Engn Ctr Johor Baharu 81310 Johor Malaysia VRG Dinamik SDN BHD Shah Alam 40170 Selangor Malaysia

Guided ultrasonic wave (GUW) monitoring systems are gaining much attention in pipeline condition monitoring. However, the effects of environmental and operational conditions (EOCs), especially temperature and random noise, degrade damage detection performance. When EOC effects produce greater amplitudes than the reflected waves from small damage cases, the reflected waves remain unidentified. This paper proposes an unsupervised learning-based denoising autoencoder (DAE) to reduce the effect of EOCs in GUW monitoring systems. A DAE decodes high-dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing GUW signals at a reference temperature, this structure forces the DAE to learn the essential features hidden within complex data. The proposed DAE undergoes comparative analysis with other popular unsupervised learning algorithms used for EOC compensation in GUW monitoring systems, such as principal component analysis, independent component analysis and deep autoencoder algorithms. EOC compensation performance is evaluated through receiver operating characteristics (ROC). From the numerical model and an experimental model, the GUW database is obtained. All four algorithms showed good damage detection performance using a numerical model;however, in the experimental model, the proposed DAE showed superiority among other methods.

关键词： Guided ultrasonic wave Unsupervised learning Environmental and operational conditions Independent component analysis Principal component analysis deep autoencoder Denoising autoencoder

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deep multiple non-negative matrix factorization for multi-view clustering

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INTELLIGENT DATA ANALYSIS 2021年第2期25卷 339-357页

作者： Du, Guowang Zhou, Lihua Lu, Kevin Ding, Haiyan Yunnan Univ Sch Informat Kunming 650091 Yunnan Peoples R China Brunel Univ Uxbridge Middx England

Multi-view clustering aims to group similar samples into the same clusters and dissimilar samples into different clusters by integrating heterogeneous information from multi-view data. Non-negative matrix factorization (NMF) has been widely applied to multi-view clustering owing to its interpretability. However, most NMF-based algorithms only factorize multi-view data based on the shallow structure, neglecting complex hierarchical and heterogeneous information in multi-view data. In this paper, we propose a deep multiple non-negative matrix factorization (DMNMF) framework based on autoencoder for multi-view clustering. DMNMF consists of multiple Encoder Components and Decoder Components with deep structures. Each pair of Encoder Component and Decoder Component are used to hierarchically factorize the input data from a view for capturing the hierarchical information, and all Encoder and Decoder Components are integrated into an abstract level to learn a common low-dimensional representation for combining the heterogeneous information across multi-view data. Furthermore, graph regularizers are also introduced to preserve the local geometric information of each view. To optimize the proposed framework, an iterative updating scheme is developed. Besides, the corresponding algorithm called MVC-DMNMF is also proposed and implemented. Extensive experiments on six benchmark datasets have been conducted, and the experimental results demonstrate the superior performance of our proposed MVC-DMNMF for multi-view clustering compared to other baseline algorithms.

关键词： deep non-negative matrix factorization multi-view clustering deep autoencoder graph regularization constraints

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Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study

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NEURAL NETWORKS 2021年 135卷 91-104页

作者： Qiao, Chen Yang, Lan Calhoun, Vince D. Xu, Zong-Ben Wang, Yu-Ping Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Georgia Inst Technol Triinst Ctr Translat Res Neuroimaging & Data Sci Atlanta GA 30303 USA Emory Univ Atlanta GA 30303 USA Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Tulane Univ Ctr Genom & Bioinformat New Orleans LA 70112 USA

Recently, the focus of functional connectivity analysis of human brain has shifted from merely revealing the inter-regional functional correlation over the entire scan duration to capturing the time-varying information of brain networks and characterizing time-resolved reoccurring patterns of connectivity. Much effort has been invested into developing approaches that can track changes in re-occurring patterns of functional connectivity over time. In this paper, we propose a sparse deep dictionary learning method to characterize the essential differences of reoccurring patterns of time varying functional connectivity between different age groups. The proposed method combines both the interpretability of sparse dictionary learning and the capability of extracting sparse nonlinear higher-level features in the latent space of sparse deep autoencoder. In other words, it learns a sparse dictionary of the original data by considering the nonlinear representation of the data in the encoder layer based on a sparse deep autoencoder. In this way, the nonlinear structure and higher-level features of the data can be captured by deep dictionary learning. The proposed method is applied to the analysis of the Philadelphia Neurodevelopmental Cohort. It shows that there exist essential differences in the reoccurrence patterns of function connectivity between child and young adult groups. Specially, children have more diffusive functional connectivity patterns while young adults possess more focused functional connectivity patterns, and the brain function transits from undifferentiated systems to specialized neural networks with the growth. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： deep dictionary learning deep autoencoder Sparsity Dynamic functional connectivity Reoccurring pattern Brain development

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DMC-Fusion: deep Multi-Cascade Fusion With Classifier-Based Feature Synthesis for Medical Multi-Modal Images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第9期25卷 3438-3449页

作者： Zuo, Qing Zhang, Jianping Yang, Yin Xiangtan Univ Sch Math & Comp Sci Xiangtan 411105 Peoples R China

Multi-modal medical image fusion is a challenging yet important task for precision diagnosis and surgical planning in clinical practice. Although single feature fusion strategy such as Densefuse has achieved inspiring performance, it tends to be not fully preserved for the source image features. In this paper, a deep multi-fusion framework with classifier-based feature synthesis is proposed to automatically fuse multi-modal medical images. It consists of a pre-trained autoencoder based on dense connections, a feature classifier and a multi-cascade fusion decoder with separately fusing high-frequency and low-frequency. The encoder and decoder are transferred from MS-COCO datasets and pre-trained simultaneously on multi-modal medical image public datasets to extract features. The feature classification is conducted through Gaussian high-pass filtering and the peak signal to noise ratio thresholding, then feature maps in each layer of the pre-trained Dense-Block and decoder are divided into high-frequency and low-frequency sequences. Specifically, in proposed feature fusion block, parameter-adaptive pulse coupled neural network and l(1) -weighted are employed to fuse high-frequency and low-frequency, respectively. Finally, we design a novel multi-cascade fusion decoder on total decoding feature stage to selectively fuse useful information from different modalities. We also validate our approach for the brain disease classification using the fused images, and a statistical significance test is performed to illustrate that the improvement in classification performance is due to the fusion. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance in both qualitative and quantitative evaluations.

关键词： deep autoencoder Feature classifier Medical image fusion Multi-cascade fusion

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Microblog Dimensionality Reduction-A deep Learning Approach

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2016年第7期28卷 1779-1789页

作者： Xu, Lei Jiang, Chunxiao Ren, Yong Chen, Hsiao-Hwa Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan

Exploring potentially useful information from huge amount of textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of microblog texts, using term frequency vectors to represent microblog texts will cause "sparse data" problem. Finding proper representations of microblog texts is a challenging issue. In this paper, we apply deep networks to map the high-dimensional representations of microblog texts to low-dimensional representations. To improve the result of dimensionality reduction, we take advantage of the semantic similarity derived from two types of microblog-specific information, namely the retweet relationship and hashtags. Two types of approaches, including modifying training data and modifying the training objective of deep networks, are proposed to make use of microblog-specific information. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.

关键词： Microblog mining dimension reduction text representation semantic relatedness deep autoencoder

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Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments

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IEEE INTERNET OF THINGS JOURNAL 2021年第12期8卷 9634-9644页

作者： Hussain, Tanveer Muhammad, Khan Ullah, Amin Del Ser, Javier Gandomi, Amir H. Sajjad, Muhammad Baik, Sung Wook de Albuquerque, Victor Hugo C. Sejong Univ Seoul 143747 South Korea Sejong Univ Dept Software Seoul 143747 South Korea Univ Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Spain Univ Basque Country UPV EHU Dept Commun Engn Bilbao 48940 Spain Basque Ctr Appl Math BCAM Bilbao 48009 Spain Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Islamia Coll Peshawar Dept Comp Sci Peshawar 25000 Pakistan Univ Fortaleza Grad Program Appl Informat BR-60811905 Fortaleza Ceara Brazil Fed Inst Educ Sci & Technol Ceara LAPISCO Fortaleza Ceara Brazil ARMTEC Tecnol Robot Fortaleza Ceara Brazil

Multiview video summarization (MVS) has not received much attention from the research community due to inter-view correlations and views' overlapping, etc. The majority of previous MVS works are offline, relying on only summary, and require additional communication bandwidth and transmission time, with no focus on foggy environments. We propose an edge intelligence-based MVS and activity recognition framework that combines artificial intelligence with Internet of Things (IoT) devices. In our framework, resource-constrained devices with cameras use a lightweight CNN-based object detection model to segment multiview videos into shots, followed by mutual information computation that helps in a summary generation. Our system does not rely solely on a summary, but encodes and transmits it to a master device using a neural computing stick for inter-view correlations computation and efficient activity recognition, an approach which saves computation resources, communication bandwidth, and transmission time. Experiments show an increase of 0.4 unit in F-measure on an MVS Office dateset and 0.2% and 2% improved accuracy for UCF-50 and YouTube 11 datesets, respectively, with lower storage and transmission times. The processing time is reduced from 1.23 to 0.45 s for a single frame and optimally 0.75 seconds faster MVS. A new dateset is constructed by synthetically adding fog to an MVS dateset to show the adaptability of our system for both certain and uncertain IoT surveillance environments.

关键词： Activity recognition deep autoencoder deep learning Internet of Things (IoT) multiview video summarization (MVS) sequential learning video data analytics video summarization

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Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第4期62卷 925-949页

作者： Cassavia, Nunziato Caviglione, Luca Guarascio, Massimo Liguori, Angelica Zuppelli, Marco Inst High Performance Comp & Networking Via Pietro Bucci 8-9-C I-87036 Arcavacata Di Rende Italy Inst Appl Math & Informat Technol Via Marini 6 I-16149 Genoa Italy Univ Calabria Via Pietro Bucci I-87036 Arcavacata Di Rende Italy

Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate resource-constrained devices within a botnet or leak sensitive information. A major research effort is then devoted to create countermeasures for mitigating attacks, for instance, hardware-level verification mechanisms or effective network intrusion detection frameworks. Unfortunately, advanced malware is often endowed with the ability of cloaking communications within network traffic, e.g., to orchestrate compromised IoT nodes or exfiltrate data without being noticed. Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an attacker deploying two hiding schemes (i.e., naive and "elusive" approaches). Collected results showcase the effectiveness of our method as well as the feasibility of deploying autoencoders in production-quality IoT settings.

关键词： deep autoencoder Ensemble method Covert channel Intelligent cyber attack detection system

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An SSA-LC-DAE Method for Extracting Network Security Elements

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第2期10卷 1175-1185页

作者： Tao, Xiaoling Liu, Ziyi Zhao, Feng Lan, Rushi Liu, Runrong Fu, Lianyou Ouyang, Yifu Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

In the current network environment, the original network data present the characteristics of multiple features and high dimensions. Furthermore, in the existing element extraction methods based on deep neural networks, the loss of feature information layer by layer increases as the data dimensions decrease, which greatly affects the retention of network data information elements and brings a huge challenge to effective network security protection. This paper refers to a residual neural network to improve the deep autoencoder (DAE) and then utilizes them to propose a novel element extraction method named the layer-by-layer loss compensation deep autoencoder (LC-DAE) based on the sparrow search algorithm (SSA). In the proposed method, a loss compensation module is added to each encoding layer of the DAE. Specifically, this module first restores the data by using the decoding layer corresponding to the encoding layer. Then, the loss value of the calculated characteristic information is compensated using the output of the corresponding encoding layer. Subsequently, the SSA is used to optimize the LC-DAE in the training process. Finally, the experimental results show that compared with the existing methods, this method retains more sufficient element information, and significantly improves the classification performance of data using neural networks.

关键词： Feature extraction Data mining Network security Encoding Decoding Neural networks deep learning deep autoencoder element extraction Index Terms network data sparrow search algorithm

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deep Multiview Clustering via Iteratively Self-Supervised Universal and Specific Space Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11734-11746页

作者： Zhang, Yue Huang, Qinjian Zhang, Bin He, Shengfeng Dan, Tingting Peng, Hong Cai, Hongmin Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Multiview clustering seeks to partition objects via leveraging cross-view relations to provide a comprehensive description of the same objects. Most existing methods assume that different views are linear transformable or merely sampling from a common latent space. Such rigid assumptions betray reality, thus leading to unsatisfactory performance. To tackle the issue, we propose to learn both common and specific sampling spaces for each view to fully exploit their collaborative representations. The common space corresponds to the universal self-representation basis for all views, while the specific spaces are the view-specific basis accordingly. An iterative self-supervision scheme is conducted to strengthen the learned affinity matrix. The clustering is modeled by a convex optimization. We first solve its linear formulation by the popular scheme. Then, we employ the deep autoencoder structure to exploit its deep nonlinear formulation. The extensive experimental results on six real-world datasets demonstrate that the proposed model achieves uniform superiority over the benchmark methods.

关键词： Feature extraction Tensors Kernel Correlation Task analysis Numerical models Training deep autoencoder multiview clustering self-supervised universal and specific space learning

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