I added several figures (3-6 through 3-9) to Part 3 of my unmixing posts, to better illustrate the performance of consensus interpretation methods.

Part 3: https://dblog.vitumbre.tech/dart/unmixing-using-nmf-part-3-assessing-accuracy-of-end-members/

#Unmixing #NMF

RE: https://floss.social/@rdnielsen/116365121149129752

The third and last of the series on unmixing using NMF is now posted at

https://dblog.vitumbre.tech/dart/unmixing-using-nmf-part-3-assessing-accuracy-of-end-members/

Part 3 illustrates the variability of results that can occur when repeatedly unmixing the same data set, and presents approaches to addressing the resultant uncertainty.

#DataAnalysis #DataExploration #Unmixing #NMF #Python #JuliaLang #RStats

RE: https://floss.social/@rdnielsen/116363795536617194

Part 2 of this series on unmixing is now available:

https://dblog.vitumbre.tech/dart/unmixing-using-nmf-part-2-evaluating-the-number-of-end-members/

Part 2 addresses the challenge of deciding how many end members are in a data set, recommends algorithms for Python, Julia, and R, and illustrates how several factors affect that determination.

#DataAnalysis #DataExploration #Unmixing #NMF #Python #JuliaLang #RStats

I just posted part 1 of a 3-part series on unmixing of data sets using non-negative matrix factorization.

https://dblog.vitumbre.tech/dart/unmixing-using-non-negative-matrix-factorization-nmf-part-1-introduction-and-implementation/

Part 1 contains implementations in Python, Julia, and R, and includes an assessment of the relative accuracy of these implementations.

Parts 2 and 3 will follow shortly, and will contain more detail on the identification of, and accurate characterization of, unmixing end members.

#DataAnalysis #DataExploration #Python #JuliaLang #RStats #Unmixing #NMF