  The problem of estimating parameters of switched affine systems with noisy
input-output observations is considered. The switched affine models is
transformed into a switched linear one by removing its intersection subspace,
which is estimated from observations. A subspace technique is proposed to
exploit the observations' permutation structure, which transforms the problem
of associating observations with subsystems into one of de-permutating a block
diagonal matrix, referred as adjacency matrix. Then a normalized spectral
clustering algorithm is presented to recover the block structure of adjacency
matrix, from which each observation is related to a particular subsystem. With
the labelled observations, parameters of the submodel are estimated via the
total least squares (TLS) estimator. The proposed technique is applicable to
switched affine systems with arbitrarily shaped domain partitions, and it
offers significantly improved performance and lowered computation complexity
than existing techniques.
