Høskuldsson, Agnar

Methods are presented that find a nonlinear subspace in low dimensions that describe data given by many variables. The methods include nonlinear extensions of Principal Component Analysis and extensions of linear regression analysis. It is shown by examples that these methods give more reliable results than similar techniques, where variables are selected and polynomials in those used

Identifying surfaces of low dimensions in high dimensional data

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