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

Salaheddin Alakkari & John Dingliana
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...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.