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    An Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Data

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    Date
    2018
    Author
    Alakkari, Salaheddin
    Dingliana, John
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    Abstract
    In this paper, we discuss the problem of decomposing complex and large Molecular Dynamics trajectory data into simple low-resolution representation using Principal Component Analysis (PCA). Since applying standard PCA for such large data is expensive in terms of space and time complexity, we propose a novel online PCA algorithm with O(1) complexity per new timestep. Our approach is able to approximate the full dimensional eigenspace per new time-step of MD simulation. Experimental results indicate that our technique provides an effective approximation to the original eigenspace computed using standard PCA in batch mode.
    BibTeX
    @inproceedings {10.2312:molva.20181100,
    booktitle = {Workshop on Molecular Graphics and Visual Analysis of Molecular Data},
    editor = {Jan Byska and Michael Krone and Björn Sommer},
    title = {{An Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Data}},
    author = {Alakkari, Salaheddin and Dingliana, John},
    year = {2018},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-061-1},
    DOI = {10.2312/molva.20181100}
    }
    URI
    http://dx.doi.org/10.2312/molva.20181100
    https://diglib.eg.org:443/handle/10.2312/molva20181100
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    • MolVa: Workshop on Molecular Graphics and Visual Analysis of Molecular Data 2018

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    Eurographics Association copyright © 2013 - 2022 
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    Theme by @mire NV
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