Selection amongst Alternative Variables in Regression Analysis
نویسنده
چکیده
This paper presents an approach to the task of regression analysis by imposing a dedicated sparseness pattern is imposed on the solution. It imposes the estimate to be expressed in terms of only one single variable amongst the proposed set of alternative variables. The task of Selection amongst Alternative variables in Regression Analysis (SARA) is elaborated for linear as well as kernel based methods using the primal-dual argument according to optimization theory. Selection of inputs amongst alternative variables is achieved through the use of a dedicated regularization scheme by shrinking the active set of variables gradually towards a singleton.
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