Robust high dimensional expectation maximization algorithm via trimmed hard thresholding
نویسندگان
چکیده
منابع مشابه
Robust Regression via Hard Thresholding
We study the problem of Robust Least Squares Regression (RLSR) where several response variables can be adversarially corrupted. More specifically, for a data matrix X ∈ Rp×n and an underlying model w∗, the response vector is generated as y = XTw∗+b where b ∈ R is the corruption vector supported over at most C ·n coordinates. Existing exact recovery results for RLSR focus solely on L1-penalty ba...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2020
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-020-05926-z