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

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dc.contributor.authorAlakkari, Salaheddinen_US
dc.contributor.authorDingliana, Johnen_US
dc.contributor.editorJan Byska and Michael Krone and Björn Sommeren_US
dc.date.accessioned2018-06-02T17:49:29Z
dc.date.available2018-06-02T17:49:29Z
dc.date.issued2018
dc.description.abstractIn 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.en_US
dc.description.sectionheadersComputational Analysis of Dynamic Molecular Data
dc.description.seriesinformationWorkshop on Molecular Graphics and Visual Analysis of Molecular Data
dc.identifier.doi10.2312/molva.20181100
dc.identifier.isbn978-3-03868-061-1
dc.identifier.pages1-8
dc.identifier.urihttps://doi.org/10.2312/molva.20181100
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/molva20181100
dc.publisherThe Eurographics Associationen_US
dc.titleAn Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Dataen_US
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MolVa: Workshop on Molecular Graphics and Visual Analysis of Molecular Data 2018