Nonlinear matrix recovery using optimization on the Grassmann manifold
We investigate the problem of recovering a partially observed high-rank matrix whose columns obey a nonlinear structure such as a union of subspaces, an algebraic variety or grouped in clusters. The recovery problem is formulated as the rank minimization of a nonlinear feature map applied to the ori...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2022
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