Visual-Empirical Region-of-Influence

This technique makes use of the innate human cognition to perform clustering in multidimensional space (Osbourn and Martinez, 1995). It is unsupervised and model-free, therefore requires from the user only the data input. A special mask mathematically defines the visual region of influence. Its shape is based on human visual perception, taking advantage of the human brain to recognize and cluster objects (Fig. 10.10a). Its properties are as follows. Two points in space are clustered only if no other point lies within area of the mask which thus defines the exclusion region. In this way, an n-dimensional problem is reduced to set of n two-dimensional problems. 

Although the shape of the Visual-Empirical Region-of-Influence (VERI) mask is invariable, it size scales automatically according to the properties of the cluster (Fig. 10.10b). The VERI algorithm requires preprocessing of the data and for that purpose PCA or PCR preprocessing is routinely used. It is relatively immune to the presence of unknowns, and nonlinearity and nonadditivity of sensor responses (Osbourn et al., 1998). It has been used successfully to determine the optimum 

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Fig. 10.10 Rejection mask used in Visual Empirical Region of Influence (VERI). The shape of the mask is mathematically derived from the visual cognitive factors (a) geometrical scaling relationships used in the derivation of the shape (b) final mask which defines the exclusion region, that is, objects that do not belong to the group (Osbourn and Martinez, 1995)...

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