Fig. 2.

Empirical estimate of $\overline{\chi }$ in (11) across iterations in the alternating optimization algorithm [1] that solves CAOL (P0) with α = 10⁻³. (The fruit and city datasets with L = 10 and N = 10⁴ were preprocessed with contrast enhancement and mean subtraction; see details of datasets and experiments in [1], [2] and references therein. The model mismatches $\left\{E_l^{(i)}:\forall l\right\}$ at the ith iteration were calculated every 50 iterations based on (3), where we use the converged filters for D_true.) Observe that ${\overline{\chi }}^{(i)}$ generally decreases over iterations; when $\overline{\chi }$ is small, the high probability error bound (9) in Theorem 3 depends primarily on $\overline{\rho }$ defined in (11).