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A deep autoencoder with sparse and graph Laplacian regularization for characterizing dynamic functional connectivity during brain development

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NEUROCOMPUTING 2021年 456卷 97-108页

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

deep-layer autoencoder (DAE) provides a powerful way for medical image analysis, while it remains a daunting challenge due to the limited samples but high dimension. In this paper, a DAE with sparse and graph Laplacian regularization, termed as GSDAE, is presented to identify significant differences of dynamic functional connectivity (dFC) between child and young adult groups. The proposed model incor-porates prior knowledge into sparse learning, i.e., the intrinsic structural information defined by manifold in the data. In this way, the reconstruction ability of unsupervised DAE can be improved, which facilitates the extraction of most discriminative features of dFC changing with age. Results on the fMRI data from the Philadelphia Neurodevelopmental Cohort project reveal essential differences lying in the reoccur-rence patterns of dFC and in the connectivity of resting state networks with increasing age, e.g., there exist different trajectories of connectivity patterns in brain functions: those associated with complex cog-nitive functions generally decreased, while those associated with basic visual or motor control functions usually enhanced. In addition, the brain circuitry moves from segregation to integration during brain development. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep autoencoder Graph regularization Sparsity Dynamic functional connectivity Brain development

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A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2017年 95卷 187-204页

作者： Shao Haidong Jiang Hongkai Zhao Huiwei Wang Fuan Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the maximum correntropy is adopted to design the new deep autoencoder loss function for the enhancement of feature learning from the measured vibration signals. Secondly, artificial fish swarm algorithm is used to optimize the key parameters of the deep autoencoder to adapt to the signal features. The proposed method is applied to the fault diagnosis of gearbox and electrical locomotive roller bearing. The results confirm that the proposed method is more effective and robust than other methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： deep autoencoder Feature learning Fault diagnosis Maximum correntropy Artificial fish swarm algorithm

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Classification of Motor Imagery EEG Signals Based on deep autoencoder and Convolutional Neural Network Approach

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IEEE ACCESS 2022年 10卷 48071-48081页

作者： Hwaidi, Jamal F. Chen, Thomas M. Univ London Dept Elect & Elect Engn London EC1V 0HB England

The technology of the brain-computer interface (BCI) employs electroencephalogram (EEG) signals to establish direct interaction between the human body and its surroundings with promising applications in medical rehabilitative services and cognitive science. deep learning approaches, particularly the detection and analysis of motor imagery signals using convolutional neural network (CNN) frameworks have produced outstanding results in the BCI system in recent years. The complex process of data representation, on the other hand, limits practical applications, and the end-to-end approach reduces the accuracy of recognition. Moreover, since noise and other signal sources can interfere with brain electrical capacitance, EEG classifiers are difficult to improve and have limited generalisation ability. To address these issues, this paper proposes a new approach for EEG motor imagery signal classification by using a variational autoencoder to remove noise from the signals, followed by a combination of deep autoencoder (DAE) and a CNN architecture to classify EEG motor imagery signals which is capable of training a deep neural network to replicate its input to output using encoding and decoding operations. Experimental results show that the proposed approach for motor imagery EEG signal classification is feasible and that it outperforms current CNN-based approaches and several traditional machine learning approaches.

关键词： Electroencephalography Feature extraction Convolutional neural networks deep learning Support vector machines Training Brain modeling Electroencephalography deep autoencoder convolutional neural network variational autoencoder motor imagery

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Image encryption based on logistic chaotic systems and deep autoencoder

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PATTERN RECOGNITION LETTERS 2022年 153卷 59-66页

作者： Sang, Yongpeng Sang, Jun Alam, Mohammad S. Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Texas A&M Univ Kingsville Frank H Dotterweich Coll Engn Kingsville TX USA

In this paper, we propose a novel image encryption method based on logistic chaotic systems and deep autoencoder. In the encryption phase, first, the plaintext image is randomly scrambled by a logistic chaotic system. Then, the random scrambled image is encoded by a deep autoencoder to generate the ciphertext image. In order to obtain the ciphertext image with uniform distribution, we incorporated the uniform distribution constraint into the training of the deep autoencoder. The resulting ciphertext image contains high randomness, which is critical for an excellent image encryption algorithm. Histogram analysis, information entropy analysis, key space analysis, key sensitivity analysis, correlation analysis, and ablation experiments show that the proposed encryption algorithm can effectively resist attacks and has excellent encryption performance while providing high security. (c) 2021 Published by Elsevier B.V.

关键词： Image encryption Logistic chaotic systems deep autoencoder Uniform distribution

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Semantics-enhanced supervised deep autoencoder for depth image-based 3D model retrieval

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PATTERN RECOGNITION LETTERS 2019年 125卷 806-812页

作者： Siddiqua, Ayesha Fan, Guoliang Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Increased accuracy and affordability of depth sensors such as Kinect has created a great depth-data source for various 3D oriented applications. Specifically, 3D model retrieval is attracting attention in the field of computer vision and pattern recognition due to its numerous applications. A cross-domain retrieval approach such as depth image based 3D model retrieval has the challenges of occlusion, noise and view variability present in both query and training data. In this paper, we propose a new supervised deep autoencoder approach followed by semantic modeling to retrieve 3D shapes based on depth images. The key novelty is the two-fold feature abstraction to cope with the incompleteness and ambiguity present in the depth images. First, we develop a supervised autoencoder to extract robust features from both real depth images and synthetic ones rendered from 3D models, which are intended to balance reconstruction and classification capabilities of mix-domain data. Then semantic modeling of the supervised autoencoder features offers the next level of abstraction to cope with the incompleteness and ambiguity of the depth data. It is interesting that unlike any other pairwise model structures, we argue that cross-domain retrieval is still possible using only one single deep network trained on real and synthetic data. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross-modal 3D model retrieval. (C) 2019 Elsevier B.V. All rights reserved.

关键词： 3D model retrieval Shape matching Cross-modal retrieval deep autoencoder

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TDEC: Evidential Clustering Based on Transfer Learning and deep autoencoder

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5585-5597页

作者： Jiao, Lianmeng Wang, Feng Liu, Zhun-Ga Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Evidential clustering is a promising clustering framework using Dempster-Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.

关键词： Clustering algorithms Partitioning algorithms Transfer learning Evidence theory Feature extraction Fuzzy systems Approximation algorithms deep autoencoder evidential clustering unsupervised transfer learning

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Community-aware dynamic network embedding by using deep autoencoder

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INFORMATION SCIENCES 2020年第0期519卷 22-42页

作者： Ma, Lijia Zhang, Yutao Li, Jianqiang Lin, Qiuzhen Bao, Qing Wang, Shanfeng Gong, Maoguo Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hangzhou Elect Sci & Technol Univ Sch Comp Sci Hangzhou 310018 Peoples R China Xidian Univ Sch Elect Engn Key Lab Intelligent Percept & Image Understanding Minist Educ 2 South TaiBai Rd Xian 710071 Peoples R China

Network embedding has recently attracted lots of attention due to its wide applications on graph tasks such as link prediction, network reconstruction, node stabilization, and community stabilization, which aims to learn the low-dimensional representations of nodes with essential features. Most existing network embedding methods mainly focus on static or continuous evolution patterns of microscopic node and link structures in networks, while neglecting the dynamics of macroscopic community structures. In this paper, we propose a Community-aware Dynamic Network Embedding method (short for CDNE) which considers the dynamics of macroscopic community structures. First, we model the problem of dynamic network embedding as a minimization of an overall loss function, which tries to maximally preserve the global node structures, local link structures, and continuous community dynamics. Then, we adopt a stacked deep autoencoder algorithm to solve this minimization problem, obtaining the low-dimensional representations of nodes. Extensive experiments on both synthetic networks and real networks demonstrate the superiority of CDNE over the existing methods on tackling various graph tasks. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Network embedding Dynamic networks Community structures deep autoencoder Low-dimensional node representation

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CDDA: color-dominant deep autoencoder for faster and efficient bilateral image filtering

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SIGNAL IMAGE AND VIDEO PROCESSING 2021年第6期15卷 1189-1195页

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

Nonlinear processing of high-dimensional data is quite common in image filtering algorithms. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge due to its computational complexity. In this paper, we have proposed a solution utilizing both color sparseness and color dominance in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency-based deep encoded dominant color gamut, learned for different subject classes of images. The proposed bilateral filter has been proved to be efficient both in terms of psycho-visual quality and performance for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with the existing fast bilateral algorithms in terms of the CTQ (critical to quality) parameters.

关键词： Range-domain filtering deep autoencoder weighted histogram fast algorithm color sparseness

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Joint network embedding of network structure and node attributes via deep autoencoder

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NEUROCOMPUTING 2022年 468卷 198-210页

作者： Pan, Yu Zou, Junhua Qiu, Junyang Wang, Shuaihui Hu, Guyu Pan, Zhisong Army Engn Univ Inst Command & Control Engn Nanjing Peoples R China Jiangnan Inst Comp Technol Math Engn & Adv Comp Wuxi Jiangsu Peoples R China

Network embedding aims to learn a low-dimensional vector for each node in networks, which is effective in a variety of applications such as network reconstruction and community detection. However, the majority of the existing network embedding methods merely exploit the network structure and ignore the rich node attributes, which tend to generate sub-optimal network representation. To learn more desired network representation, diverse information of networks should be exploited. In this paper, we develop a novel deep autoencoder framework to fuse topological structure and node attributes named FSADA. We firstly design a multi-layer autoencoder which consists of multiple non-linear functions to capture and preserve the highly non-linear network structure and node attribute information. Particularly, we adopt a pre-processing procedure to pre-process the original information, which can bet -ter facilitate to extract the intrinsic correlations between topological structure and node attributes. In addition, we design an enhancement module that combines topology and node attribute similarity to construct pairwise constraints on nodes, and then a graph regularization is introduced into the frame-work to enhance the representation in the latent space. Our extensive experimental evaluations demon-strate the superior performance of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep autoencoder deep learning Network analysis Network embedding Data mining Pattern recognition

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IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第27期83卷 69049-69081页

作者： Dewangan, Somesh Kumar Choubey, Siddharth Patra, Jyotiprakash Choubey, Abha Shri Shankaracharya Tech Campus Dept Comp Sci Engn Bhilai 490001 Chhattisgarh India Shri Shankaracharya Inst Profess Management & Tech Dept Comp Sci Engn Raipur 492015 Chhattisgarh India

The effective restoration of degraded remote sensing images is one of the major concerns as it directly affects imaging system performance. In recent years, investigators have developed innumerable systems and methods for remotely sensed hazy or blurred image restoration to enhance the system's performance. However, the existing remotely sensed image restoration system and methods still have untold limitations such as texture degradation, high computational complexity, preparative parametric tunning, more time consumption in restoration procedure, lesser accuracy, and many more. Hence, effective and fast restoration of the degraded remote sensing images with more accuracy is still a challenging problem, which requires enormous attention toward a novel image restoration approach. In this work, the researchers implement a remote sensing image restoration framework namely IMU-CNN, which is based on mask-upgraded cascade R-CNN and deep autoencoder. Our proposed IMU-CNN model performs hazy or blurred remotely sensed image restoration in a faster manner with improved accuracy, as well as saves time in the image restoration process, significantly. The outcome of the proposed image restoration framework is found improved and optimal and measured accuracy, precision, F1 score, and Recall is 99.59%, 97.24%, 97.69%, and 96.49%, respectively. Recently, researchers have conducted much investigation on remotely sensed image restoration systems by using various hybrid approaches. However, still, there is a need for further investigation in the future for building sophisticated and less computationally complex systems for effective remotely sensed image restoration in less time.

关键词： Cascade R-CNN deep autoencoder Image restoration IMU-CNN Remote sensing

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