Sparse power factorization: balancing peakiness and sample complexity
Sparse power factorization: balancing peakiness and sample complexity
复制标题
稀疏功率因数分解：平衡峰值和样本复杂度
DOI：
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发表时间：
2018
影响因子：
1.7
通讯作者：
Dominik Stöger
中科院分区：
文献类型：
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作者：
Jakob Geppert;F. Krahmer;Dominik Stöger
在许多应用中，人们面临着一个逆问题，其中已知信号以双线性方式依赖于两个未知输入向量。通常假设输入向量中至少有一个是稀疏的，即只有很少的非零项。由Lee， Wu和Bresler提出的稀疏功率分解（SPF）旨在解决这一问题。他们在假设测量是随机的情况下，为某种程度上受限的信号建立了恢复保证。我们以适度增加测量次数为代价，将这些恢复保证推广到显着扩大和更现实的信号类别。
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.