Meta Sparse Principal Component Analysis
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We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the sufficient sample complexity in a novel task with the information that is learned from auxiliary tasks. We assume each task to be a different random Principal Component (PC) matrix with a possibly different support and that the support union of the PC matrices is small. We then pool the data from all the tasks to execute an improper estimation of a single PC matrix by maximising the l_1-regularised predictive covariance to establish that with high probability the true support union can be recovered provided a sufficient number of tasks m and a sufficient number of samples O(log(p)/m) for each task, for p-dimensional vectors. Then, for a novel task, we prove that the maximisation of the l_1-regularised predictive covariance with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to O(log |J|), where J is the support union recovered from the auxiliary tasks. Typically, |J| would be much less than p for sparse matrices. Finally, we demonstrate the validity of our experiments through numerical simulations.
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