Outcome from NMFk analysis. (A) The Silhouette-Reconstruction

Download scientific diagram | Outcome from NMFk analysis. (A) The Silhouette-Reconstruction criterium (see the Methods Section). On the x-axis is denoted the number of basis lipid configurations and on the y-axis-the average Silhouettes (right y-axis, red marking) as from publication: Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes | Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that are capable to decompose and | Unsupervised Machine Learning, Lipids and Biological Membranes | ResearchGate, the professional network for scientists.

Sensors, Free Full-Text

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ShiftNMFk results for identification of the locations and speed of

Schematic of the NMFk procedure. The initial data set X is re-sampled

Outcome from NMFk analysis. (A) The Silhouette-Reconstruction criterium

ShiftNMFk algorithm correctly identifying the number of sources

Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes

Unsupervised Pharmaceutical Polymorph Identification and Multicomponent Particle Mapping of ToF-SIMS Data by Non-Negative Matrix Factorization

AIC and Silhouette values computed for an example X matrix using NMFk

Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture

Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization - Alexandrov - 2014 - Water Resources Research - Wiley Online Library