Optimal supports for linear predictive models
Rajesh Rajagopalan, M.T. Orchard, Kannan Ramchandran · IEEE Transactions on Signal Processing · 1996
The problem of finding the optimal set of causal pixels (support) for use in linear predictive models is addressed. After presenting counterexamples to popular intuitions about supports, a general result relating the distortion incurred with a small support to optimal coefficients of a larger support is derived. A geometrical interpretation is provided. Two algorithms that optimally increase/decrease support sizes by one at each step are presented. Experimental results illustrate the significant gains realized by the algorithms compared with commonly used supports.