提出一种新方法，从矩阵中快速找到正交子矩阵。
Isometry pursuit
- 用新归一化+多任务稀疏优化找正交子矩阵
- 在坐标选择任务中效果优于贪心和暴力搜索
- 适合需要正交性与可解释性的机器学习场景
等距追求是一种凸算法，用于识别宽矩阵的正交列子矩阵。该方法包含一种新型归一化策略，随后进行多任务基追踪。将其应用于疑似坐标函数的雅可比矩阵时，可从可解释字典中识别出等距嵌入。论文提供了理论和实验结果支持该方法。在涉及坐标选择与多样化的任务中，它为贪心和暴力搜索提供了一种协同替代方案。
原文摘要 · Abstract (English)
Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization method followed by multitask basis pursuit. Applied to Jacobians of putative coordinate functions, it helps identity isometric embeddings from within interpretable dictionaries. We provide theoretical and experimental results justifying this method. For problems involving coordinate selection and diversification, it offers a synergistic alternative to greedy and brute force search.
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