Research review for broad learning system: Algorithms, theory, and applications
In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …
revolutionize conventional artificial intelligence methods. It represents a step toward building …
Semi-supervised and un-supervised clustering: A review and experimental evaluation
K Taha - Information Systems, 2023 - Elsevier
Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …
efficient mechanism for overcoming these challenges is to cluster the data into a compact …
A new subspace clustering strategy for AI-based data analysis in IoT system
Z Cui, X Jing, P Zhao, W Zhang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …
Unsupervised self-correlated learning smoothy enhanced locality preserving graph convolution embedding clustering for hyperspectral images
Y Ding, Z Zhang, X Zhao, W Cai, N Yang… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
Hyperspectral image (HSI) clustering is an extremely fundamental but challenging task with
no labeled samples. Deep clustering methods have attracted increasing attention and have …
no labeled samples. Deep clustering methods have attracted increasing attention and have …
Hyperspectral image unsupervised classification by robust manifold matrix factorization
Hyperspectral remote sensing image unsupervised classification, which assigns each pixel
of the image into a certain land-cover class without any training samples, plays an important …
of the image into a certain land-cover class without any training samples, plays an important …
Contrastive multi-view subspace clustering of hyperspectral images based on graph convolutional networks
High-dimensional and complex spectral structures make the clustering of hyperspectral
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
images (HSIs) a challenging task. Subspace clustering is an effective approach for …
Hyperspectral image denoising using local low-rank matrix recovery and global spatial–spectral total variation
Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as
stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their …
stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their …
Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing
Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their
corresponding fractional abundances, is an important task for hyperspectral analysis …
corresponding fractional abundances, is an important task for hyperspectral analysis …
Deep spatial-spectral subspace clustering for hyperspectral image
Hyperspectral image (HSI) clustering is a challenging task due to the complex
characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large …
characteristics in HSI data, such as spatial-spectral structure, high-dimension, and large …
Hyperspectral image restoration using low-rank tensor recovery
This paper studies the hyperspectral image (HSI) denoising problem under the assumption
that the signal is low in rank. In this paper, a mixture of Gaussian noise and sparse noise is …
that the signal is low in rank. In this paper, a mixture of Gaussian noise and sparse noise is …