Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA
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| Format: | Preprint |
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2025
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| _version_ | 1866911065834520576 |
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| author | Kumar, Syamantak Pandey, Shourya Sarkar, Purnamrita |
| author_facet | Kumar, Syamantak Pandey, Shourya Sarkar, Purnamrita |
| contents | We propose a novel statistical inference framework for streaming principal component analysis (PCA) using Oja's algorithm, enabling the construction of confidence intervals for individual entries of the estimated eigenvector. Most existing works on streaming PCA focus on providing sharp sin-squared error guarantees. Recently, there has been some interest in uncertainty quantification for the sin-squared error. However, uncertainty quantification or sharp error guarantees for entries of the estimated eigenvector in the streaming setting remains largely unexplored. We derive a sharp Bernstein-type concentration bound for elements of the estimated vector matching the optimal error rate up to logarithmic factors. We also establish a Central Limit Theorem for a suitably centered and scaled subset of the entries. To efficiently estimate the coordinate-wise variance, we introduce a provably consistent subsampling algorithm that leverages the median-of-means approach, empirically achieving similar accuracy to multiplier bootstrap methods while being significantly more computationally efficient. Numerical experiments demonstrate its effectiveness in providing reliable uncertainty estimates with a fraction of the computational cost of existing methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_12655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA Kumar, Syamantak Pandey, Shourya Sarkar, Purnamrita Statistics Theory Machine Learning We propose a novel statistical inference framework for streaming principal component analysis (PCA) using Oja's algorithm, enabling the construction of confidence intervals for individual entries of the estimated eigenvector. Most existing works on streaming PCA focus on providing sharp sin-squared error guarantees. Recently, there has been some interest in uncertainty quantification for the sin-squared error. However, uncertainty quantification or sharp error guarantees for entries of the estimated eigenvector in the streaming setting remains largely unexplored. We derive a sharp Bernstein-type concentration bound for elements of the estimated vector matching the optimal error rate up to logarithmic factors. We also establish a Central Limit Theorem for a suitably centered and scaled subset of the entries. To efficiently estimate the coordinate-wise variance, we introduce a provably consistent subsampling algorithm that leverages the median-of-means approach, empirically achieving similar accuracy to multiplier bootstrap methods while being significantly more computationally efficient. Numerical experiments demonstrate its effectiveness in providing reliable uncertainty estimates with a fraction of the computational cost of existing methods. |
| title | Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA |
| topic | Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2506.12655 |