Quantification of Differential Information using Matrix Pencil
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Any traditional classification problem in general involves modelling individual classes and in turn classification by evaluating the similarity of the test set with the modelled classes. In this paper, we introduce another approach that would find the differential information between two classes rather than modelling individual classes separately. The classes are viewed on a common frame of reference in which one class would have a constant variance, unlike the other class which would have unequal variance along its basis vectors which would capture the differential information of one class over the other.This, when mathematically formulated, leads to the solution of Matrix Pencil equation.The theory of binary classification was extended to a multi-class scenario.This is borne out by illustrative examples on the classification of the MNIST database.
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