Here is the abstract:
In this presentation we discuss the application of different dimension reduction algorithms over collections of random mandalas. We discuss and compare the derived image bases and show how those bases explain the underlying collection structure. The presented techniques and insights (1) are applicable to any collection of images, and (2) can be included in larger, more complicated machine learning workflows. The former is demonstrated with a handwritten digits recognition
application; the latter with the generation of random Bethlehem stars. The (parallel) walk-through of the core demonstration is in all three programming languages: Mathematica, Python, and R.
Here is the related RStudio project: “RandomMandalasDeconstruction”.
Here is a link to the R-computations notebook converted to HTML: “LSA methods comparison in R”.
The Mathematica notebooks are placed in project’s folder “notebooks-WL”.
See the work plan status in the org-mode file “Random-mandalas-deconstruction-presentation-work-plan.org”.
Here is the mind-map for the presentation:
The comparison workflow implemented in the notebooks of this project is summarized in the following flow chart:
[AA1] Anton Antonov, “Comparison of dimension reduction algorithms over mandala images generation”, (2017), MathematicaForPrediction at WordPress.
[AA2] Anton Antonov, “Handwritten digits recognition by matrix factorization”, (2016), MathematicaForPrediction at WordPress.
Mathematica packages and repository functions
[AAp1] Anton Antonov, Monadic Latent Semantic Analysis Mathematica package, (2017), MathematicaForPrediction at GitHub/antononcube.