Weighted Residual NMF With Spatial Regularization for Hyperspectral Unmixing
IEEE Geoscience and Remote Sensing Letters, 2022 This letter proposes a weighted residual nonnegative matrix factorization (NMF) with spatial regularization to unmix hyperspectral (HS) data. NMF decomposes a matrix into the product of two nonnegative matrices.
Taner Ince, N. Dobigeon
semanticscholar +1 more source
Global-Local Enhancement Network for NMF-Aware Sign Language Recognition [PDF]
ACM Trans. Multim. Comput. Commun. Appl., 2020 Sign language recognition (SLR) is a challenging problem, involving complex manual features (i.e., hand gestures) and fine-grained non-manual features (NMFs) (i.e., facial expression, mouth shapes, etc.). Although manual features are dominant, non-manual
Hezhen Hu +3 more
semanticscholar +1 more source
Analyze the robustness of three NMF algorithms (Robust NMF with L1 norm, L2-1 norm NMF, L2 NMF)
, 2023 22 pages, 6 ...
Zeng, Cheng, Tian, Jiaqi, Xu, Yixuan
openaire +2 more sources
Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 more
doaj +1 more source
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Xin-Ru Feng +5 more
doaj +1 more source
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing [PDF]
Signal Processing, 2020 Blind spectral unmixing is the problem of decomposing the spectrum of a mixed signal or image into a collection of source spectra and their corresponding activations indicating the proportion of each source present in the mixed spectrum.
V. Leplat +2 more
semanticscholar +1 more source
Proteogenomic characterization of cholangiocarcinoma
Hepatology, EarlyView., 2022 Proteogenomic characterization of cholangiocarcinoma with therapeutic strategies Abstract Background and Aims Cholangiocarcinoma (CCA) is a highly heterogeneous cancer with limited understanding and few effective therapeutic approaches. We aimed at providing a proteogenomic CCA characterization to inform biological processes and treatment ...
Mengjie Deng +18 more
wiley +1 more source
Multiplicative Updates for NMF with β-Divergences under Disjoint Equality Constraints [PDF]
SIAM Journal on Matrix Analysis and Applications, 2020 Nonnegative matrix factorization (NMF) is the problem of approximating an input nonnegative matrix, $V$, as the product of two smaller nonnegative matrices, $W$ and $H$. In this paper, we introduce a general framework to design multiplicative updates (MU)
V. Leplat, Nicolas Gillis, J. Idier
semanticscholar +1 more source
Single channel speech music separation using nonnegative matrix factorization and spectral masks [PDF]
, 2011 A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization ...
Erdogan, Hakan +2 more
core +1 more source
Blind Audio Source Separation With Minimum-Volume Beta-Divergence NMF [PDF]
IEEE Transactions on Signal Processing, 2019 Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider in this paper the blind audio source separation problem which consists in isolating and extracting each of the sources.
V. Leplat, Nicolas Gillis, A. Ang
semanticscholar +1 more source