稀疏功率分解：在尖峰性和样本复杂性之间平衡
Sparse Power Factorization: Balancing peakiness and sample complexity
AI总结：
本文提出稀疏功率分解方法，通过增加测量数量来扩展信号类，提升稀疏信号恢复的鲁棒性。
摘要：
在许多应用中，人们面临逆问题，其中已知信号以双线性方式依赖于两个未知输入向量。通常至少有一个输入向量被假设为稀疏的，即只有少数非零元素。稀疏功率分解（SPF）由Lee、Wu和Bresler提出，旨在解决此问题。他们为某种限制性的信号类建立了恢复保证，假设测量是随机的。我们扩展这些恢复保证到显著扩大且更现实的信号类，以牺牲适度增加的测量数量。
英文摘要：
In many applications, one is faced with an inverse problem, where the known signal depends in a bilinear way on two unknown input vectors. Often at least one of the input vectors is assumed to be sparse, i.e., to have only few non-zero entries. Sparse Power Factorization (SPF), proposed by Lee, Wu, and Bresler, aims to tackle this problem. They have established recovery guarantees for a somewhat restrictive class of signals under the assumption that the measurements are random. We generalize these recovery guarantees to a significantly enlarged and more realistic signal class at the expense of a moderately increased number of measurements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
