Embedding based Regression in Nonlinear System
نویسندگان
چکیده
Analyzing a nonlinear system on high dimensions is not simple, especially with the practical applications. Since the prediction of the output from the dynamical system can be formulated as a regression problem, many previous attempts have been made to solve this regression. With the assumption of nonlinearity in the system, we attempt to apply encoding method, such as sparse coding or local coordinate coding (LCC), to unwrap the nonlinear included in the inputs, and then the linear regression method is used afterwards. We also presented a novel encoding method that is deriven from the locality constrianing idea from LCC, which is called soft assignment clustering. In order to validate the robustness of each combination of methods, the swiss-roll data set and the internal combustion engine (ICE) data set with noise are analyzed.
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