European Journal of Scientific Research - EuroJournals

430 Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad,
Hafizah Husain and Mohd Yusof Jamaluddin
KG rule that suggested retaining all eigenvalues > 1 results in thirty-five PCs to be considered as
significant components. The PCs are as tabulated in Table 1.
Table 1: The significant eigenvalues or PCs using the KG rule.
Factor k 1 2 3 4 5
Eigenvalue 44.37 23.64 17.48 12.25 10.32
Factor k 6 7 8 9 10
Eigenvalue 9.37 5.77 5.64 5.21 3.89
Factor k 11 12 13 14 15
Eigenvalue 3.79 3.17 3.07 2.9 2.64
Factor k 16 17 18 19 20
Eigenvalue 2.6 2.48 2.39 2.19 2.02
Factor k 21 22 23 24 25
Eigenvalue 1.97 1.77 1.71 1.67 1.63
Factor k 26 27 28 29 30
Eigenvalue 1.57 1.42 1.35 1.28 1.26
Factor k 31 32 33 34 35
Eigenvalue 1.2 1.14 1.11 1.06 1.01
Figure 3: Percentage of the total variance accounted by each PCs or eigenvalues using Scree Test.
Percent of total variance (%)
20
18
16
14
12
10
8
6
4
2
0
0 5 10 15 20
Factor No k
25 30 35 40
Further, we consider the cumulative variance rule of thumb as our feature selection basis to
determine the optimum number of eigenvalues or PCs. Figure 4a depicts the overall cumulative
variance of the eigenvalues of the orthogonal eigenvectors of the human posture database produced in
this study. The blue solid line represents the cumulative variance whilst the dotted line represents the t
threshold of 90% for selecting the optimal number of PCs to retain. As suggested in [8], a threshold t
of between 80%-90% can be considered to determine factor number, k. In this case, a 90% criterion
would result in k equals 70, thus suggesting that 70 PCs are required to account for more than 90% of
the total variance and an 80% criterion would result in k equals 34 as depicts in Figure 4b.
Consequently, all three rules of thumb provide constructive results and in this study, we rule
that thirty-five PCs are adequate to represent a large part of the variance of the original human postures
data set. The eigenvectors of these thirty-five PCs that we termed as eigenpostures will undergo the
statistical analysis prior to classification. Accordingly, we determine the statistical significance of all
the thirty-five eigenpostures of the four main postures using ANOVA. In this analysis, null hypothesis