Factor Augmented High-Dimensional SGD
Stochastic gradient descent (SGD) is a fundamental optimization algorithm widely used in modern machine learning. In this paper, we propose Factor-Augmented SGD (FSGD), a new optimization method that leverages latent factor representations in high-dimensional learning tasks. Unlike standard two-stage dimension reduction approaches that rely on offline representation learning and full data storage, a key novelty of FSGD is that it operates purely on streaming data, making it scalable to large-scale and high-dimensional problems. Furthermore, we establish the first theoretical framework that explicitly incorporates latent factor estimation error into the analysis of SGD, and provide moment ...
| Authors | Shubo Li, Yuefeng Han, Xiufan Yu |
|---|---|
| Primary category | stat.ML |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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