Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review
Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
A fast non-negative latent factor model based on generalized momentum method
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
Generalized nesterov's acceleration-incorporated, non-negative and adaptive latent factor analysis
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …
An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications
High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-
related and industrial applications like recommender systems. When acquiring useful …
related and industrial applications like recommender systems. When acquiring useful …
An overview of blind source separation methods for linear-quadratic and post-nonlinear mixtures
Whereas most blind source separation (BSS) and blind mixture identification (BMI)
investigations concern linear mixtures (instantaneous or not), various recent works extended …
investigations concern linear mixtures (instantaneous or not), various recent works extended …
Convergence analysis of single latent factor-dependent, nonnegative, and multiplicative update-based nonnegative latent factor models
A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU)
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …
Non-negative latent factor model based on β-divergence for recommender systems
Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS)
matrices filled with non-negative data, which are frequently encountered in industrial …
matrices filled with non-negative data, which are frequently encountered in industrial …
Efficient extraction of non-negative latent factors from high-dimensional and sparse matrices in industrial applications
High-dimensional and sparse (HiDS) matrices are commonly encountered in many big data-
related industrial applications like recommender systems. When acquiring useful patterns …
related industrial applications like recommender systems. When acquiring useful patterns …
Estimation of soil and crop residue parameters using AVIRIS-NG hyperspectral data
I Majeed, NK Purushothaman… - … Journal of Remote …, 2023 - Taylor & Francis
Detailed ground cover information and efficient modelling approaches are needed for
estimating soil properties from hyperspectral remote sensing (HRS) data. With the objective …
estimating soil properties from hyperspectral remote sensing (HRS) data. With the objective …
Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data
High-spectral-resolution hyperspectral data are acquired by sensors that gather images from
hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer …
hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer …