In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer
In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to
capture much more information from a certain scene, both within and beyond the visual …
capture much more information from a certain scene, both within and beyond the visual …
Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review
This paper critically reviews all artificial intelligence (AI) and machine learning (ML)
techniques for the better control of membrane fouling in filtration processes, with the focus …
techniques for the better control of membrane fouling in filtration processes, with the focus …
The why and how of nonnegative matrix factorization
N Gillis - … , optimization, kernels, and support vector machines, 2014 - books.google.com
Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …
high-dimensional data as it automatically extracts sparse and meaningful features from a set …
SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering
Nonnegative matrix factorization (NMF) provides a lower rank approximation of a matrix by a
product of two nonnegative factors. NMF has been shown to produce clustering results that …
product of two nonnegative factors. NMF has been shown to produce clustering results that …
[LIVRE][B] Nonnegative matrix factorization
N Gillis - 2020 - SIAM
Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …
Hyperspectral remote sensing benchmark database for oil spill detection with an isolation forest-guided unsupervised detector
Oil spill detection has attracted increasing attention in recent years, since marine oil spill
accidents severely affect environments, natural resources, and the lives of coastal …
accidents severely affect environments, natural resources, and the lives of coastal …
Hyperspectral image clustering: Current achievements and future lines
Hyperspectral remote sensing organically combines traditional space imaging with
advanced spectral measurement technologies, delivering advantages stemming from …
advanced spectral measurement technologies, delivering advantages stemming from …
Unsupervised segmentation of hyperspectral remote sensing images with superpixels
In this paper, we propose an unsupervised method for hyperspectral remote sensing image
segmentation. The method exploits the mean-shift clustering algorithm that takes as input a …
segmentation. The method exploits the mean-shift clustering algorithm that takes as input a …
Self-supervised learning-based oil spill detection of hyperspectral images
Oil spill monitoring in remote sensing field has become a very popular technology to detect
the spatial distribution of polluted regions. However, previous studies mainly focus on the …
the spatial distribution of polluted regions. However, previous studies mainly focus on the …
Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion
The problem of unsupervised learning and segmentation of hyperspectral images is a
significant challenge in remote sensing. The high dimensionality of hyperspectral data …
significant challenge in remote sensing. The high dimensionality of hyperspectral data …