Suchergebnisse - nonnegativity-constraint sparse stacked denoising autoencoder

A Nonnegativity-Constraint Sparse Stacked Denoising Autoencoder for Anomaly Detection in Electric Power Communication Network

Autoren: Zhuo Tao Yuanyuan Wang Yang Yang et al.

Quelle: 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

Schlagwörter: 9. Industry and infrastructure, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions.

Autoren: Di, ZiYang Shao, HaiDong Xiang, JiaWei

Quelle: SCIENCE CHINA Technological Sciences; Mar2021, Vol. 64 Issue 3, p481-492, 12p

A hybrid stacked sparse autoencoder and LightGBM framework for high-performance intrusion detection in IoT networks.

Autoren: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

Quelle: Iran Journal of Computer Science; Dec2025, Vol. 8 Issue 4, p2175-2198, 24p

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing.

Autoren: Zhao, Chong Wang, Jinlin Qiao, Qingqing et al.

Quelle: Remote Sensing; Nov2025, Vol. 17 Issue 21, p3622, 28p

Schlagwörter: AUTOENCODERS, GEOLOGICAL surveys, HYPERSPECTRAL imaging systems, SURFACE properties, REMOTE-sensing images, PROSPECTING

Graph Regularized Sparse Autoencoders with Nonnegativity Constraints.

Autoren: Teng, Yueyang Liu, Yichao Yang, Jinliang et al.

Quelle: Neural Processing Letters; Aug2019, Vol. 50 Issue 1, p247-262, 16p

Schlagwörter: CONSTRAINT programming, SPARSE graphs

Window Transformer Convolutional Autoencoder for Hyperspectral Sparse Unmixing.

Autoren: Kong, Fanqiang Zheng, Yuhan Li, Dan et al.

Quelle: IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-5, 5p

Supervised Self-Encoder-Based Feature Learning Study for Fault Diagnosis of Variable Operating Condition Rotor Blade Systems.

Autoren: Zhang, Qinglei Niu, Xueying Duan, Jianguo et al.

Quelle: Journal of Combinatorial Mathematics & Combinatorial Computing; Dec2025, Vol. 127a, p923-946, 24p

Schlagwörter: STEAM-turbine blades, TURBOMACHINE blade vibration, VIBRATION (Mechanics), FEATURE extraction, ARTIFICIAL neural networks

Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction.

Autoren: Hajim, Wesam Ibrahim Zainudin, Suhaila Daud, Kauthar Mohd et al.

Quelle: PeerJ Computer Science; Dec2024, p1-36, 36p

Schlagwörter: CONVOLUTIONAL neural networks, LONG short-term memory, DEEP learning, GOLDEN eagle, AUTOENCODERS

Discriminative Feature Learning via Sparse Autoencoders with Label Consistency Constraints.

Autoren: Hu, Cong Wu, Xiao-Jun Shu, Zhen-Qiu

Quelle: Neural Processing Letters; Oct2019, Vol. 50 Issue 2, p1079-1091, 13p

Schlagwörter: LABELS, CONSTRAINT algorithms, DETERMINISTIC algorithms, HIERARCHICAL Bayes model, INFORMATION networks, ARCHITECTURE

Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices.

Autoren: Wu, Pengxia Cheng, Julian

Quelle: IEEE Transactions on Wireless Communications; Oct2022, Vol. 21 Issue 10, p8090-8105, 16p

A Novel Joint Change Detection Approach Based on Weight-Clustering Sparse Autoencoders.

Autoren: Fan, Jianchao Lin, Kai Han, Min

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing; Feb2019, Vol. 12 Issue 2, p685-699, 15p

A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing.

Autoren: Zhang, Mingle Yang, Mingyu Xie, Hongyu et al.

Quelle: Remote Sensing; Sep2024, Vol. 16 Issue 17, p3149, 26p

Schlagwörter: CONVOLUTIONAL neural networks, DATA mining, FEATURE extraction, PIXELS, NOISE, DEEP learning, AUTOENCODERS

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery.

Autoren: Shao, Haidong Xia, Min Wan, Jiafu et al.

Quelle: IEEE/ASME Transactions on Mechatronics; Feb2022, Vol. 27 Issue 1, p24-33, 10p

Snapshot High Dynamic Range Imaging via Sparse Representations and Feature Learning.

Autoren: Fotiadou, Konstantina Tsagkatakis, Grigorios Tsakalides, Panagiotis

Quelle: IEEE Transactions on Multimedia; Mar2020, Vol. 22 Issue 3, p688-703, 16p

Visualizing and Understanding Nonnegativity Constrained Sparse Autoencoder in Deep Learning.

Autoren: Ayinde, Babajide O. Hosseini-Asl, Ehsan Zurada, Jacek M.

Quelle: Artificial Intelligence & Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part I; 2016, p3-14, 12p

Autoencoder Network for Hyperspectral Unmixing With Adaptive Abundance Smoothing.

Autoren: Hua, Ziqiang Li, Xiaorun Qiu, Qunhui et al.

Quelle: IEEE Geoscience & Remote Sensing Letters; Sep2021, Vol. 18 Issue 9, p1640-1644, 5p

Robust Deep Learning Architecture for Traffic Flow Estimation from a Subset of Link Sensors.

Autoren: Owais, Mahmoud Moussa, Ghada S. Hussain, Khaled F.

Quelle: Journal of Transportation Engineering. Part A. Systems; Jan2020, Vol. 146 Issue 1, p1-13, 13p, 5 Diagrams, 3 Charts, 4 Graphs

Schlagwörter: TRAFFIC flow, TRAFFIC estimation, TRANSPORTATION planning, ARCHITECTURE, TRAFFIC engineering, DEEP learning

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse‐autoencoder‐based deep neural network.

Autoren: Shah, Maqsood Hussain Dang, Xiaoyu

Quelle: IET Communications (Wiley-Blackwell); Mar2019, Vol. 13 Issue 4, p423-432, 10p

Schlagwörter: ARTIFICIAL neural networks, ARTIFICIAL intelligence, DEEP learning, ORDER statistics, SIGNAL classification

A deep contractive autoencoder for solving multiclass classification problems.

Autoren: Aamir, Muhammad Mohd Nawi, Nazri Wahid, Fazli et al.

Quelle: Evolutionary Intelligence; Dec2021, Vol. 14 Issue 4, p1619-1633, 15p

Auto-Encoders in Deep Learning—A Review with New Perspectives.

Autoren: Chen, Shuangshuang Guo, Wei

Quelle: Mathematics (2227-7390); Apr2023, Vol. 11 Issue 8, p1777, 54p

Schlagwörter: DEEP learning, PATTERN recognition systems, COMPUTER vision, MACHINE learning, FEATURE extraction, RECOMMENDER systems