Search Results - Nonnegativity-constrained autoencoder

Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data

Authors: Jie Chen ZhongCheng Wu Jun Zhang

Source: IEEE Transactions on Intelligent Transportation Systems. 20:4450-4465

Subject Terms: 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://ieeexplore.ieee.org/document/8617709/
https://dblp.uni-trier.de/db/journals/tits/tits20.html#ChenWZ19
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8617709

Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder

Authors: Zhongcheng Wu Jie Chen Jun Zhang

Source: Applied Soft Computing. 74:1-9

Subject Terms: 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Learning a referenceless stereopair quality engine with deep nonnegativity constrained sparse autoencoder

Authors: Qiuping Jiang Feng Shao Gangyi Jiang et al.

Source: Pattern Recognition. 76:242-255

Subject Terms: 9. Industry and infrastructure, 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data

Authors: Ayinde, Babajide O. Zurada, Jacek M.

Index Terms: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/1802.00003

Visualizing and Understanding Nonnegativity Constrained Sparse Autoencoder in Deep Learning

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

Source: Lecture Notes in Computer Science ISBN: 9783319393773

Access URL: https://link.springer.com/chapter/10.1007/978-3-319-39378-0_1
https://dblp.uni-trier.de/db/conf/icaisc/icaisc2016-1.html#AyindeHZ16
https://link.springer.com/chapter/10.1007/978-3-319-39378-0_1/fulltext.html
https://rd.springer.com/chapter/10.1007/978-3-319-39378-0_1
https://link.springer.com/10.1007/978-3-319-39378-0_1

Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder.

Authors: Chen, Jie Wu, ZhongCheng Zhang, Jun

Source: Applied Soft Computing. Jan2019, Vol. 74, p1-9. 9p.

Subject Terms: Identification, Automobile drivers, Feature extraction, Deep learning, Random forest algorithms

Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data.

Authors: Chen, Jie Wu, ZhongCheng Zhang, Jun

Source: IEEE Transactions on Intelligent Transportation Systems; Dec2019, Vol. 20 Issue 12, p4450-4465, 16p

A variational autoencoder for probabilistic non-negative matrix factorisation.

Authors: Squires, Steven Prügel Bennett, Adam Niranjan, Mahesan

Source: Pattern Analysis & Applications; Dec2025, Vol. 28 Issue 4, p1-10, 10p

Visualizing and Understanding Nonnegativity Constrained Sparse Autoencoder in Deep Learning.

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

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

Cross-covariance regularized autoencoders for nonredundant sparse feature representation

Authors: Fang Li Wu Ziheng Wenjing Li et al.

Source: Neurocomputing. 316:49-58

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://www.sciencedirect.com/science/article/abs/pii/S0925231218308853
https://dblp.uni-trier.de/db/journals/ijon/ijon316.html#ChenWZLLW18
https://doi.org/10.1016/j.neucom.2018.07.050

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

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

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

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

Milling tool condition monitoring for difficult-to-cut materials based on NCAE and IGWO-SVM.

Authors: Wang, Siqi Yan, Shichao Sun, Yuwen

Source: International Journal of Advanced Manufacturing Technology. Nov2023, Vol. 129 Issue 3/4, p1355-1374. 20p.

Subject Terms: *GREY Wolf Optimizer algorithm, *OPTIMIZATION algorithms, *FEATURE extraction, *SUPPORT vector machines, *FREQUENCY-domain analysis

Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.

Authors: Ayinde, Babajide O. Zurada, Jacek M.

Source: Neural Networks. Sep2017, Vol. 93, p99-109. 11p.

Subject Terms: *FEATURE extraction, *DEEP learning, *CLUSTER analysis (Statistics), *PATTERN recognition systems, *ARTIFICIAL neural networks

Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints.

Authors: Hosseini-Asl, Ehsan Zurada, Jacek M. Nasraoui, Olfa

Source: IEEE Transactions on Neural Networks & Learning Systems; Dec2016, Vol. 27 Issue 12, p2486-2498, 13p

Subject Terms: NEURAL circuitry, DEEP learning, KNOWLEDGE representation (Information theory)

A New Framework to Train Autoencoders Through Non-Smooth Regularization.

Authors: Amini, Sajjad Ghaemmaghami, Shahrokh

Source: IEEE Transactions on Signal Processing; 4/1/2019, Vol. 67 Issue 7, p1860-1874, 15p

Subject Terms: FEATURE extraction, SIGNAL processing

Discriminative Feature Learning via Sparse Autoencoders with Label Consistency Constraints.

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

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

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

A body welded joint quality prediction method incorporating reconstruction error.

Authors: Chen, Xiangxiang Sheng, Buyun Fu, Gaocai et al.

Source: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (Sage Publications, Ltd.); Mar2025, Vol. 239 Issue 6, p1936-1952, 17p

Graph Regularized Sparse Autoencoders with Nonnegativity Constraints.

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

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

Subject Terms: CONSTRAINT programming, SPARSE graphs

Integrating Dual Graph Constraints into Sparse Non-Negative Tucker Decomposition for Enhanced Co-Clustering.

Authors: Han, Jing Lu, Linzhang

Source: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 21, p3494, 25p

Subject Terms: MACHINE learning, CLUSTER analysis (Statistics), IMAGE recognition (Computer vision), MATRIX decomposition, MATHEMATICAL optimization, MATHEMATICAL regularization

From Spatial Domain to Patch-Based Models: A Comprehensive Review and Comparison of Multimodal Medical Image Denoising Algorithms.

Authors: Sharma, Apoorav Dogra, Ayush Goyal, Bhawna et al.

Source: Computers, Materials & Continua; 2025, Vol. 85 Issue 1, p367-481, 115p

Subject Terms: IMAGE processing, SIGNAL denoising, NOISE control, RANDOM noise theory, COMPUTER-assisted image analysis (Medicine), NOISE, DIAGNOSIS