发表机构
National Chung Hsing University; Institute of Data Science and Information Computing（中兴大学; 数据科学与信息计算研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高相干字典的物理意义不确定性，提出感知分辨率的物理支撑推理方法，引入自适应有限组的AEB算法，实验显示其能避免无支撑细化且评估候选更少。
AI 中文摘要
在字典学习的稀疏追踪中，即便校准数据未为其赋予物理解释，仍能生成精确的原子支撑，对于高相干字典而言尤为如此——这类字典中，与校准兼容的替代字典可能为同一选定支撑赋予不同物理意义。我们提出了感知分辨率的物理支撑推理方法，该方法同时考虑了学习到的字典和部署信号表示的不确定性。我们的跨字典置信对应关系保留了与校准兼容的字典和与部署兼容的稀疏表示，随后将幸存的解释投影到物理支撑空间。对于分离尺度为s的局部相干原子类，一旦部署数据解析了相干块解释及其原子支撑，来自N个校准信号的极小极大物理分辨率满足δ_opt(N,s)∝min{s,1/(√N s²)}，相对分辨率由取向信息尺度Ns⁶控制。仅当取向变化无法通过调整活动系数吸收时，部署复制才能改善物理定位。在计算方面，我们引入了活动端点 bracketing（AEB），这是一种自适应有限组过程，仅评估仍能影响物理报告的候选，否则安全地粗化或弃权（不执行）。包括四区域合成应用在内的有限组实验表明，点值插件选择器在物理上可能过于精确，而AEB以更少的候选评估避免了无支撑的细化。
英文摘要
Sparse pursuit after dictionary learning can yield a precise atom support even when its physical interpretation is not justified by the calibration data, especially for highly coherent dictionaries where alternative calibration-compatible dictionaries may assign different physical meanings to the same selected support. We develop resolution-aware physical-support inference that jointly accounts for uncertainty in the learned dictionary and in the representation of a deployment signal. Our cross-dictionary confidence correspondence retains calibration-compatible dictionaries and deployment-compatible sparse representations, then projects the surviving explanations onto physical-support space. For local coherent-atom classes with separation scale s, once the deployment data resolve the coherent-block explanation and its atom support, the minimax physical resolution from N calibration signals satisfies $δ_{\mathrm{opt}}(N,s)\asymp\min\{s,\frac{1}{\sqrt{N}s^2}\}$, with relative resolution governed by the orientation-information scale $Ns^6$. Deployment replication improves physical localization only when orientation changes cannot be absorbed by adjusting the active coefficients. For computation, we introduce active endpoint bracketing (AEB), an adaptive finite-bank procedure that evaluates only candidates that can still affect the physical report and otherwise safely coarsens or abstains. Finite-bank experiments, including a four-region synthetic application, show that a point-valued plug-in selector can be physically overprecise, whereas AEB avoids unsupported refinement with fewer candidate evaluations.
