Hadamard wirtinger flow for sparse phase retrieval
We consider the problem of reconstructing an n-dimensional k-sparse signal from a set of noiseless magnitude-only measurements. Formulating the problem as an unregularized empirical risk minimization task, we study the sample complexity performance of gradient descent with Hadamard parametrization,...
| Principais autores: | , |
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| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
Journal of Machine Learning Research
2021
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