Master Thesis - Department of Computer Science

5.3.1 The Algorithm
The steps of our algorithm are as follows:
1. For i= 1,2,..., L do
1.1 Evaluate performance of classifier Di on validation response vector set
RV
V A
i
and denote it by P DIR(RV
Di
1.2 Use train response vector set RV
T R
i
V A
i
based on LDA or nonparametric LDA.
1.3 Evaluate the performance of classifier Di on RV
).
of classifier Di to construct eigenmodel
V A
i
V A
constructed in previous step. For m = 1, 2, ..., Mi select each Di(x mc ) from set RV
V A
i
and do the following:
in the eigenmodel
and c = 1, 2, ..., C,
(a) Project Di(x mc ) on LDA or nonparametric LDA-based eigenmodel.
(b) Calculate the class scores by measuring the Euclidean distance from
Di(x mc ) to the nearest sample from every class in the eigenmodel to
generate ¯ Di(x mc ).
(c) Use minimum membership rule on ¯ Di(x mc ) to produce crisp class
label for x mc .
(d) Cross check the generated class label with actual class label of x mc .
Calculate the performance accuracy on RV
If P EM
Di
V A (RVi ) > P DIR
Di (RV
continue for next classifier.
V A
i
V A
i
and denote it by P EM
Di
V A (RVi ).
) then continue, else go back to step 1.1 to
1.4 For an unknown test sample x, select Di(x) from set RV
¯Di(x) using the following steps.
T S
i
and calculate
(a) Project Di(x) on LDA or nonparametric LDA-based eigenmodel gen-
erated in step 1.2.
(b) Calculate the new class scores for x as ¯ Di(x) by measuring the Eu-
clidean distance from Di(x) to the nearest response vector from every
class in the eigenspace.
The elements of RV
T S
i
are now ¯ Di(x) instead of Di(x).
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