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