Object class, prior knowledge

In the class discrimination methods or hyperplane techniques, of which linear discriminant analysis and the linear learning machine are examples, the equation of a plane or hyperplane is calculated that separates one class from another. These methods work well if prior knowledge allows the analyst to assume that the test objects must... 

It is important to realize, however, that this similarity is a coincidence of the case. Jansson s boundedness constraint always tends toward smoothness near the bounds, regardless of a priori knowledge of object class, whereas our ML estimator [Eqs. (17) and (19)] can be constrained toward bumpy solutions, the bumps consistent with the prior knowledge at hand. In the high-conviction case that we are treating here, the bumps would be manifest in the inputs Qm. We next study the effect of varying the Qm in some computer simulations. 

Clustering methodologies represent unsupervised analyses that are not appropriate for the incorporation of prior knowledge about the observations. Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another but are very dissimilar to objects... 

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