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Abstract:
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In this paper, we propose a new regularization technique to estimate the coefficient image in a image-on-scalar regression model. We have developed a locally sparse estimator when the value of the coefficient image is zero within certain sub-regions. At the same time, the estimator has the ability to explicitly account for the piecewise smooth nature of most images. The ADMM is used to estimate the unknown coefficient images. Simulation and real data analysis have shown a superior performance of our method against many existing approaches. The distributed algorithm is also developed to handle big data.
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