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...
متن کاملRobust 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 L1penalty b...
متن کاملRobust Regression via Heuristic Hard Thresholding
The presence of data noise and corruptions recently invokes increasing attention on Robust Least Squares Regression (RLSR), which addresses the fundamental problem that learns reliable regression coefficients when response variables can be arbitrarily corrupted. Until now, several important challenges still cannot be handled concurrently: 1) exact recovery guarantee of regression coefficients 2...
متن کاملStructured Sparse Regression via Greedy Hard Thresholding
Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups. For very large datasets and under standard sparsity constraints, hard thresholding methods have proven to be extremely efficient, but such methods require NP hard projections when dealing with overlapping groups. In this paper, we show ...
متن کاملAccelerated iterative hard thresholding
The iterative hard thresholding algorithm (IHT) is a powerful and versatile algorithm for compressed sensing and other sparse inverse problems. The standard IHT implementation faces two challenges when applied to practical problems. The step size parameter has to be chosen appropriately and, as IHT is based on a gradient descend strategy, convergence is only linear. Whilst the choice of the ste...
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ژورنال
عنوان ژورنال: Scandinavian Journal of Statistics
سال: 2018
ISSN: 0303-6898,1467-9469
DOI: 10.1111/sjos.12353