Admm-mcp framework for sparse recovery with global convergence

Sparse multiple instance learning with non-convex penalty

Y Zhang, H Zhang, Y Tian - Neurocomputing, 2020 - Elsevier
Multiple instance learning (MIL) usually plays an important role in weakly labeled learning. It
is able to reduce cost noted by accurate annotations and remove noisy samples with …
被引用次数：10 相关文章
[PDF] ieee.org

Recursive Least Squares with Minimax Concave Penalty Regularization for Adaptive System Identification

B Li, S Wu, EE Tripp, A Pezeshki, V Tarokh - IEEE Access, 2024 - ieeexplore.ieee.org
We develop a recursive least squares (RLS) type algorithm with a minimax concave penalty
(MCP) for adaptive identification of a sparse tap-weight vector that represents a …
相关文章 所有 2 个版本
[PDF] arxiv.org

Minimax concave penalty regularized adaptive system identification

B Li, S Wu, EE Tripp, A Pezeshki, V Tarokh - arXiv preprint arXiv …, 2022 - arxiv.org
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty
(MCP) for adaptive identification of a sparse tap-weight vector that represents a …
被引用次数：1 相关文章 所有 2 个版本
[PDF] arxiv.org

Revealing Cluster Structures Based on Mixed Sampling Frequencies

Y Rho, Y Liu, HJ Ahn - arXiv preprint arXiv:2004.09770, 2020 - arxiv.org
This paper proposes a new linearized mixed data sampling (MIDAS) model and develops a
framework to infer clusters in a panel regression with mixed frequency data. The linearized …
相关文章 所有 11 个版本