1 citations · 1 across the 3 of their papers we have counts for
6 papers · 1 filter
SPP-SBL: Space-Power Prior Sparse Bayesian Learning for Block Sparse Recovery
Yanhao Zhang, Zhihan Zhu, Yong Xia
The recovery of block-sparse signals with unknown structural patterns remains a fundamental challenge in structured sparse signal reconstruction. By proposing a variance transforma…
Subgradient Gliding Method for Nonsmooth Convex Optimization
Zhihan Zhu, Yanhao Zhang, Yong Xia
We identify and analyze a fundamental limitation of the classical projected subgradient method in nonsmooth convex optimization: the inevitable failure caused by the absence of val…
From Generality to Specificity: Prior-Driven Optimal Sparse Transformation in Compressed Sensing
Zhihan Zhu, Yanhao Zhang, Yong Xia
This paper introduces a new paradigm for sparse transformation: the Prior-to-Posterior Sparse Transform (POST) framework, designed to overcome long-standing limitation on generaliz…
Lipschitz-free Projected Subgradient Method with Time-varying Step-size
Yong Xia, Yanhao Zhang, Zhihan Zhu
We introduce a novel family of time-varying step-sizes for the classical projected subgradient method, offering optimal ergodic convergence. Importantly, this approach does not dep…
Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination
Zhihan Zhu, Yanhao Zhang, Yong Xia
This paper introduces two novel criteria: one for feature selection and another for feature elimination in the context of best subset selection, which is a benchmark problem in sta…
Convergence Rate of Projected Subgradient Method with Time-varying Step-sizes
Zhihan Zhu, Yanhao Zhang, Yong Xia
We establish the optimal ergodic convergence rate for the classical projected subgradient method with a time-varying step-size. This convergence rate remains the same even if we sl…