Results 1 to 10 of about 3,902,266 (287)

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding [PDF]

Frontiers in Neuroscience, 2023
IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant ...
Shaorong Zhang, Shaorong Zhang, Qihui Wang, Benxin Zhang, Zhen Liang, Li Zhang, Linling Li, Gan Huang, Zhiguo Zhang, Bao Feng, Tianyou Yu   +10 more
doaj   +2 more sources

Domain Alignment Embedding Network for Sketch Face Recognition

IEEE Access, 2021
Sketch face recognition refers to the process of matching sketches to photos. Recently, there has been a growing interest in using deep learning to learn discriminative features for sketch face recognition. However, the success of deep learning relies on
Yanan Guo, Lin Cao, Changwu Chen, Kangning Du, Chong Fu   +4 more
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Two-Branch Convolutional Neural Network with Polarized Full Attention for Hyperspectral Image Classification

Remote Sensing, 2023
In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are still insufficiently addressed, such as the receptive field problem, small ...
Haimiao Ge, Liguo Wang, Moqi Liu, Yuexia Zhu, Xiaoyu Zhao, Haizhu Pan, Yanzhong Liu   +6 more
doaj   +1 more source

Cross Task Modality Alignment Network for Sketch Face Recognition

Frontiers in Neurorobotics, 2022
The task of sketch face recognition refers to matching cross-modality facial images from sketch to photo, which is widely applied in the criminal investigation area.
Yanan Guo, Yanan Guo, Lin Cao, Lin Cao, Kangning Du, Kangning Du   +5 more
doaj   +1 more source

Improved Physics-Informed Neural Networks Combined with Small Sample Learning to Solve Two-Dimensional Stefan Problem

Entropy, 2023
With the remarkable development of deep learning in the field of science, deep neural networks provide a new way to solve the Stefan problem. In this paper, deep neural networks combined with small sample learning and a general deep learning framework ...
Jiawei Li, Wei Wu, Xinlong Feng
doaj   +1 more source

A Color/Illuminance Aware Data Augmentation and Style Adaptation Approach to Person Re-Identification

IEEE Access, 2021
Person re-identification problems usually suffer from large subject appearance variations and limited training data. This paper proposes a novel physically motivated Color/Illuminance-Aware data-augmentation (CIADA) scheme and a style-adaptive fusion ...
Zhouchi Lin, Chenyang Liu, Wenbo Qi, S. C. Chan   +3 more
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Small Sample Modulation Recognition Algorithm Based on Depth Cascade Siamese Network [PDF]

Jisuanji gongcheng, 2021
The recognition accuracy of traditional modulation recognition algorithms based on deep learning is reduced in the case of small sample size.To solve the problem,this paper proposes a small sample modulation recognition algorithm for communication signal
FENG Lei, JIANG Lei, XU Hua, GOU Zezhong
doaj   +1 more source

Marginal Fisher Analysis With Polynomial Matrix Function

IEEE Access, 2022
Marginal fisher analysis (MFA) is a dimensionality reduction method based on a graph embedding framework. In contrast to traditional linear discriminant analysis (LDA), which requires the data to follow a Gaussian distribution, MFA is suitable for non ...
Ruisheng Ran, Zheng Li, Ting Wang, Ji Feng, Bin Fang   +4 more
doaj   +1 more source

Adaptive Residual Life Prediction for Small Samples of Mechanical Products Based on Feature Matching Preprocessor-LSTM

Applied Sciences, 2022
In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching ...
Yongming Liu, Junyu Song, Zhuanzhe Zhao, Guowen Ye, Zhibo Liu, Yang Zhou   +5 more
doaj   +1 more source

Practical Small-Sample Asymptotics for Regression Problems [PDF]

Journal of the American Statistical Association, 1996
Abstract Saddlepoint approximations are derived for sums of independent, but not necessarily identically distributed random variables, along with generalizations to estimating equations and multivariate problems. These results are particularly useful for accurately approximating the distribution of regression coefficients.
Robert L. Strawderman, George Casella, Martin T. Wells   +2 more
openaire   +1 more source