Subspace clustering on parameter estimation of switched affine models

The problem of estimating parameters of switched affine systems with noisy input-output observations is considered. 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. Then a spectral clustering algorithm is presented to recover the block structure of observations, 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.