Results 81 to 90 of about 54,863 (259)
, 2016 Nonnegative Matrix Factorization(NMF) is a common used technique in machine learning to extract features out of data such as text documents and images thanks to its natural clustering properties. In particular, it is popular in image processing since it can decompose several pictures and recognize common parts if they're located in the same position ...
openaire +2 more sources
Advanced Intelligent Discovery, EarlyView.A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque +7 more
wiley +1 more source
Latitude: A Model for Mixed Linear-Tropical Matrix Factorization
, 2018 Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components ...
Hook, James +2 more
core +1 more source
A deep matrix factorization method for learning attribute representations [PDF]
, 2015 Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix
Bousmalis, Konstantinos +3 more
core +3 more sources
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
EJNMMI Physics, 2023 Background Kinetic modeling in positron emission tomography (PET) requires measurement of the tracer plasma activity in the absence of a suitable reference region.
Alan Miranda +3 more
doaj +1 more source
Frontiers in Genetics, 2023 Early diagnosis and treatment of glaucoma are challenging. The discovery of glaucoma biomarkers based on gene expression data could potentially provide new insights for early diagnosis, monitoring, and treatment options of glaucoma.
Xiaoqin Huang +8 more
doaj +1 more source
Transformation-invariant representation and NMF [PDF]
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005 Non-negative matrix factorization (NMF) is a method for the decomposition of multivariate data into strictly positive activations and basis vectors. Here, instead of using unstructured data vectors, we assume that something is known in advance about the type of transformations that either the input data or the basis vectors may undergo.
J. Eggert, H. Wersing, E. Korner
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Scientific Reports, 2017 The secondary and tertiary structure of keratin and natural moisturizing factor (NMF) are of great importance regarding the water regulating functions in the stratum corneum (SC).
C. Choe +3 more
semanticscholar +1 more source
Advanced Intelligent Systems, EarlyView.gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley +1 more source