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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a

R Kannan^1,2, A V Ievlev^1,3, N Laanait^1,3

¹1The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA.

Advanced Structural and Chemical Imaging

|May 15, 2018

Summary

This summary is machine-generated.

This study introduces matrix factorization for spectral unmixing in scientific imaging. It provides a framework and examples for scientists to interpret complex spectroscopic data more effectively.

Keywords:

Big data High performance Image segmentation Matrix factorization Scanning probe microscopy Unmixing

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Area of Science:

Materials Science
Physics
Chemistry
Spectroscopic Imaging

Background:

Spectral responses often arise from the superposition of multiple basic spectra.
Unmixing aims to identify individual spectra and their spatial abundance maps from mixed measurements.

Purpose of the Study:

To introduce linear unmixing techniques, specifically matrix factorization, to domain scientists.
To facilitate a deeper understanding of spectroscopic imaging data.
To present a flexible matrix factorization framework adaptable to various domain-specific constraints.

Main Methods:

Utilizing matrix factorization as a linear unmixing technique.
Incorporating domain-specific information through adjustable parameters in the matrix factorization framework.
Applying constraints such as non-negativity and sum-to-one abundance for physically meaningful results.

Main Results:

Demonstrated the expressivity of the matrix factorization framework with domain-specific examples.
Showcased how incorporating constraints leads to more interpretable spectral decompositions.
Highlighted the utility of an open-source implementation for broad scientific adoption.

Conclusions:

Matrix factorization offers a powerful and adaptable approach for spectral unmixing in scientific research.
The framework enables more accurate and interpretable analysis of complex spectroscopic imaging data.
The provided open-source tools promote wider accessibility and application across disciplines.