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

Alakkari, Salaheddin; Dingliana, John

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

Files

001-008.pdf (21.13 MB)

Date

2018

Authors

Alakkari, Salaheddin
Dingliana, John

Publisher

The Eurographics Association

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.

        @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
}
}