# Meta Sparse Principal Component Analysis

@article{Banerjee2022MetaSP, title={Meta Sparse Principal Component Analysis}, author={Imon Banerjee and Jean Honorio}, journal={ArXiv}, year={2022}, volume={abs/2208.08938} }

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…

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