On relative convergence properties of principal component analysis algorithms.
We investigate the convergence properties of two different stochastic approximation algorithms for principal component analysis, and analytically explain some commonly observed experimental results. In our analysis, we use the theory of stochastic approximation, and in particular the results of Fabian, to explore the asymptotic mean square errors (AMSE's) of the algorithms. This study reveals the conditions under which the algorithms produce smaller AMSE's, and also the conditions under which one algorithm has a smaller AMSE than the other. Experimental study with multidimensional Gaussian data corroborate our analytical findings. We next explore the convergence rates of the two algorithms. Our experiments and an analytical explanation reveals the conditions under which the algorithms converge faster to the solution, and also the conditions under which one algorithm converges faster than the other. Finally, we observe that although one algorithm has a larger computation in each iteration, it leads to a smaller AMSE and converges faster for the minor eigenvectors when compared to the other algorithm.