  Previous partial permutation synchronization (PPS) algorithms, which are
commonly used for multi-object matching, often involve computation-intensive
and memory-demanding matrix operations. These operations become intractable for
large scale structure-from-motion datasets. For pure permutation
synchronization, the recent Cycle-Edge Message Passing (CEMP) framework
suggests a memory-efficient and fast solution. Here we overcome the restriction
of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for
estimating the corruption levels of the observed partial permutations. It
allows us to subsequently implement a nonconvex weighted projected power method
without the need of spectral initialization. The resulting new PPS algorithm,
MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse
matrix operations, and thus enjoys lower time and space complexities in
comparison to previous PPS algorithms. We prove that under adversarial
corruption, though without additive noise and with certain assumptions,
CEMP-Partial is able to exactly classify corrupted and clean partial
permutations. We demonstrate the state-of-the-art accuracy, speed and memory
efficiency of our method on both synthetic and real datasets.
