Optimal Rates for Adaptive Private k-PCA
3d
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Given n i.i.d. random matrices A_i {R}^{d d} with common expectation Σ, the goal of Differentially Private Stochastic PCA is to identify a k-dimensional subspace capturing the leading variance directions of Σ, while preserving differential privacy~(DP) for each individual sample A_i. {dungler2025iterative} introduced , the first algorithm to simultaneously (1) achieve sample complexity n=O(d) for sub-Gaussian data, (2) adapt its privacy noise to the intrinsic randomness of the data, and (3) extend seamlessly to any target dimension kd. However, its sample complexity has a suboptimal dependence on k. We propose the first algorithm that achieves optimal sample complexity in both d and k, while retaining properties (2) and (3) of . Additionally, prior utility guarantees require a minimum sample size with exponential dependence on the eigengap. Our method removes this dependence. It also improves the dependence on spectral parameters, matching known lower bounds for the spiked covariance model. Unlike deflation-based approaches like , our algorithm updates the full dk subspace jointly rather than one eigenvector at a time. This non-deflation structure simplifies the algorithm, reduces the number of hyper-parameters, and improves computational efficiency, particularly when using block linear algebra libraries.
Autores
Johanna Düngler, Amartya Sanyal
Categorías
overfeed.news indexa y enlaza. Publicamos un extracto breve; el artículo completo sigue en arXiv AI Papers.
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