Master Thesis - Department of Computer Science

validation1, validation2 and test. Train set along with validation1, validation2 and
test set will create train, validation and test response vector sets respectively in
classifier output space. “Train response vector set” is used to build up an LDA
or nonparametric LDA-based eigenmodel. “Validation response vector set” is used
to check the usefulness of eigenmodel for improving class-separability and thereby
checking the availability of class-specific contextual information present in the output
of a classifier. If the LDA-based eigenmodel is observed to improve the performance of
a classifier for the “validation response vector set”, we recalculate class score values
(by replacing Di(x) with ¯ Di(x) in DP(x)) for “test response vector set” for that
classifier. Otherwise, all Di(x)’s from “test response vector set” remain unchanged
for that classifier.
This means that our method operates on DP(x) row-wise (classifier-wise) to pro-
duce a improved version of DP(x), denoted by ¯
DP (x). We can easily visualize our
approach as a preprocessing step before final fusion, which takes each row of DP(x)
(i.e. Di(x)) as input and replaces with Hi(x), where Hi(x) can be ¯ Di(x) or Di(x)
depending on the presence of class-specific information in the output of the corre-
sponding classifier. In the next section, we will describe how we exploit availability
of class-specific information present in classifier output space to strengthen base clas-
sifier and thereby improve the combined performance.
5.3 Improving Combined Performance by Strengthening Base Classifiers:
Proposed Method
Based on the concepts described at the end of the previous section, the strength of a
base classifier is enhanced as follows:
Use the train response vectors as intermediate feature vectors to build a eigen-
model based on LDA and nonparametric LDA at classifier output space. Then cal-
culate improved response vectors ¯ D for “test response vector set” in that eigenspace.
Theoretically, improvement of ¯ D over D is evident from the fact that LDA builds
an eigenmodel where class response vectors will be well-clustered as well as well-
separated. Empirically, it is tested by performing a performance evaluation of this
eigenmodel on a “validation response vector set”.
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