  A generic fast method for object classification is proposed. In addition, a
method for dimensional reduction is presented. The presented algorithms have
been applied to real-world data from chip fabrication successfully to the task
of predicting defect states of tens of thousands of chips of several products
based on measurements or even just part of measurements. Unlike typical neural
networks with a large number of weights to optimize over, the presented
algorithm tries optimizing only over a very small number of variables in order
to increase chances to find a global optimum. Our approach is interesting in
that it is fast, led to good to very good performance with real-world wafer
data, allows for short implementations and computes values which have a clear
meaning easy to explain.
