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

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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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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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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Efficient quality enhancement of gastrointestinal endoscopic video by a novel method of color salient bilateral filtering

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第4期80卷 6235-6245页

作者： Das, Apurba Shylaja, S. S. PES Univ Comp Sci & Engn Bangalore Karnataka India

The recent advancements in bio-photonics enabled physicians to combine techniques such as narrow-band imaging, fluorescence spectroscopy, optical coherence tomography, with visible spectrum endoscopy video to provide in vivo microscopic tissue characterization in online optical biopsy (Ye et al.2015);(Wang and Van Dam2004). Despite the aforementioned advantages, it is challenging for gastroenterologists to retarget the optical biopsy sites during endoscopic examinations because of the degraded quality of endoscopic video which gets corrupted by haze, noise, oversaturated illumination, etc. Enhancement of video frames by considering color channels independently gives birth to unintended phantom color due to its ignorance of the psycho-visual correspondence. To address the aforementioned, we have proposed a novel algorithm to enhance video with faster performance. The proposedC(2)D(2)A(Cross Color Dominant deep autoencoder) uses the strength of (a) bilateral filtering both in spatial neighborhood domain and psycho-visual range;(b) deep autoencoder which learns salient patterns. The domain-based color sparseness has further improved the performance, modulating classical deep autoencoder to color dominant deep autoencoder. The work has shown promise towards not only a generic framework of quality enhancement of video streams but also addressing performance. The current work in turn improves the image and video analytics like segmentation, detection, and tracking the objects or regions of interest.

关键词： Endoscopy deep autoencoder Color sparseness Bilateral filtering

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Research on heart and lung sound separation method based on DAE-NMF-VMD

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2024年第1期2024卷 59页

作者： Sun, Wenhui Zhang, Yipeng Chen, Fuming Chinese Peoples Liberat Army Med Secur Ctr 940th Hosp Joint Logist Support Force Lanzhou 730050 Gansu Peoples R China Gansu Univ Tradit Chinese Med Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China

Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.

关键词： Heart and lung sound separation deep autoencoder Variational mode decomposition Nonnegative matrix factorization deep learning

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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

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

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

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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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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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Dynamic Predictive Maintenance Scheduling Using deep Learning Ensemble for System Health Prognostics

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IEEE SENSORS JOURNAL 2021年第23期21卷 26878-26891页

作者： Chen, Chuang Zhu, Zheng Hong Shi, Jiantao Lu, Ningyun Jiang, Bin Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China York Univ Lassonde Sch Engn Toronto ON M3J 1P3 Canada Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China

Modern engineering systems are usually equipped with a variety of sensors to measure real-time operating conditions. Based on the condition monitoring data from multiple sensor sources, this paper deals with a dynamic predictive maintenance scheduling using deep learning ensemble for system health prognostics. The deep learning ensemble model is composed of deep autoencoder and bidirectional long short-term memory in series, and aims to accurately estimate the system health state and remaining useful life. The deep autoencoder is used to extract the deep representative features hidden in condition monitoring data, whereas the inclusion of the bidirectional long short-term memory allows learning the temporal correlation information of features in both forward and backward time directions. Thus, the combination of the two models forms an effective model. With obtained prognostic information, optimal maintenance decisions are determined by two designed rules. The first rule presets a reliable remaining useful life threshold to dynamically judge whether maintenance is carried out at current inspection time, while the second one selects a degradation state to dynamically determine whether to place a spare part order. The performance of the proposed dynamic predictive maintenance strategy is measured by the maintenance cost per unit operating time (cost rate) using the aero-engine dataset from NASA. Its effectiveness is demonstrated by comparing with recent publications.

关键词： Predictive maintenance Sensors Feature extraction Predictive models Degradation Data mining Condition monitoring Predictive maintenance health state assessment remaining useful life prediction deep autoencoder bidirectional long short-term memory maintenance cost rate

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Separation of Metabolites and Macromolecules for Short-TE ¹H-MRSI Using Learned Component-Specific Representations

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第4期40卷 1157-1167页

作者： Li, Yahang Wang, Zepeng Sun, Ruoyu Lam, Fan Univ Illinois Dept Bioengn Urbana IL 61801 USA Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA

Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI) allows for simultaneously mapping a number of molecules in the brain, and has been recognized as an important tool for studying in vivo biochemistry in various neuroscience and disease applications. However, separation of the metabolite and macromolecule (MM) signals present in the short-TE data with significant spectral overlaps remains a major technical challenge. This work introduces a new approach to solve this problem by integrating imaging physics and representation learning. Specifically, a mixed unsupervised and supervised learning-based strategy was developed to learn the metabolite and MM-specific low-dimensional representations using deep autoencoders. A constrained reconstruction formulation is proposed to integrate the MRSI spatiospectral encoding model and the learned representations as effective constraints for signal separation. An efficient algorithm was developed to solve the resulting optimization problem with provable convergence. Simulation and experimental results have been obtained to demonstrate the component-specific representation power of the learned models and the capability of the proposed method in separating metabolite and MM signals for practical short-TE H-1-MRSI data.

关键词： Proton (H-1) magnetic resonance spectroscopic imaging short TE signal separation deep learning deep autoencoder low-dimensional models

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