Sangil Han

2 accepted papers

2025

Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness

AISTATS 2025poster

In this paper, we explore the theoretical properties of subspace recovery using Winsorized Principal Component Analysis (WPCA), utilizing a common data transformation technique that caps extreme values to mitigate the impact of outliers. Despite the widespread use of winsorization in various tasks o…

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2024

Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis

Sangil Han, Sungkyu Jung, Kyoowon Kim

AISTATS 2024poster

The singular value decomposition (SVD) is a crucial tool in machine learning and statistical data analysis. However, it is highly susceptible to outliers in the data matrix. Existing robust SVD algorithms often sacrifice speed for robustness or fail in the presence of only a few outliers. This study…

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