Publication

Streaming Principal Component Analysis From Incomplete Data

Armin Eftekhari
2019
Journal Articles

Abstract

Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecting low-dimensional linear structure from incomplete data efficiently, in terms of both computational complexity and storage.

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Streaming Principal Component Analysis From Incomplete Data

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