Input reduction in human sensation modeling using independent component analysis

We model human sensations in virtual reality applications using cascade neural networks. In the modeling process, the dimension of inputs presented to the humans and the sensation systems may be very high. In this research we propose using the independent component analysis (ICA) to achieve input reduction. We obtain human sensation data from a full-body motion virtual reality interface - "motion-based movie". A fixed-point ICA algorithm is applied to achieve feature extraction and input selection for reducing the dimension of the environmental stimulus data. The fidelity of the sensation models trained using the reduced inputs is verified by the hidden Markov model based similarity measure. The performance of input reduction using ICA is compared with that using the principal component analysis. Experimental results showed that the input selection scheme based on ICA is capable of improving the modeling performance of the computational sensation systems and reducing the input dimension by 60%.