UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of …
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of …
X Zhao, M Jia, M Lin - Measurement, 2020 - Elsevier
Generally, the measured health condition data from mechanical system often exhibits imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the …
Semi-supervised learning methods using Generative adversarial networks (GANs) have shown promising empirical success recently. Most of these methods use a shared …
Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The generative adversarial network (GAN) is a promising technique to mitigate the small sample …
J Feng, H Yu, L Wang, X Cao… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Generative adversarial networks (GANs) are famous for generating samples by training a generator and a discriminator via an adversarial procedure. For hyperspectral image …
Deep neural networks have been used widely to learn the latent structure of datasets, across modalities such as images, shapes, and audio signals. However, existing models are …
J Feng, X Wu, R Shang, C Sui, J Li… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) have demonstrated outstanding performance on image classification. To classify the hyperspectral images (HSIs), existing CNN-based …