USING INDEPENDENT COMPONENT ANALYSIS AND HIGHER ORDER STATISTICS

In real world problems of nonlinear model building there may be a number of inputs available for use. However, a common problem is that we do not know which inputs are necessary for the model. Previous methods have difficulties in coping with dependent inputs. In this paper, we propose a novel method of input variable selection based on independent component analysis and higher order cross statistics. Experimental results indicate that the method is capable of giving reliable performance with dependent inputs to nonlinear models.

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