A tractable approach for 1-bit compressed sensing on manifolds
Compressed Sensing deals with reconstructing some unknown vector from few linear measurements in
high dimension by additionally assuming sparsity, i.e. many entries are zero. Recent results
guaranteed recovery even when just signs of the measurements are available (one-bit CS). A natural
generalization of classical CS replaces sparse vectors by vectors lying on manifolds having low
intrinsic dimension. In this talk I introduce the one-bit problem and proposes a tractable strategy
to solve one-bit CS problems for data lying on manifolds. This is based on joint work with Johannes Maly and Mark Iwen.