Adaptive PCA for TimeVarying Data
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Adaptive PCA for TimeVarying Data
In this paper, we present an online adaptive PCA algorithm that is able to compute the full dimensional eigenspace per new timestep of sequential data. The algorithm is based on a onestep update rule that considers all second order correlations between previous samples and the new timestep. Our algorithm has O(n) complexity per new timestep in its deterministic mode and O(1) complexity per new timestep in its stochastic mode. We test our algorithm on a number of timevarying datasets of different physical phenomena. Explained variance curves indicate that our technique provides an excellent approximation to the original eigenspace computed using standard PCA in batch mode. In addition, our experiments show that the stochastic mode, despite its much lower computational complexity, converges to the same eigenspace computed using the deterministic mode.
Adaptive PCA for TimeVarying Data
by Salaheddin Alakkari, John Dingliana
https://arxiv.org/pdf/1709.02373v1.pdf
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