Analysis reveals computational approaches for autoregressive processes in orthogonal spaces, suggesting improved system estimation.
System identification of autoregressive processes on Stiefel and Grassmann manifolds are presented and studied. We define the system parameters as elements in the orthogonal group and we show that the system can be estimated by averaging over observations. Then we propose an algorithm on how to compute these system parameters using conjugate gradient descent on Stiefel and Grassmann manifolds, respectively.
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Figueras et al. (2025) studied this question.
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