### Table 7: The classification accuracy of the original and reduced dimensional data sets.

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### Table 7: The classification accuracy of the original and reduced dimensional data sets.

2000

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### Table 7: The classification accuracy of the original and reduced dimensional data sets.

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### Table 7: The classification accuracy of the original and reduced dimensional data sets.

2000

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### Table 4. Dimensionality reduction time taken by each manifold method (seconds)

"... In PAGE 11: ... The technique of cross validation was applied to split the microarray data sets into training and testing data sets. Table4 shows the times needed for each manifold method to reduce the dimensionality of the data sets. As seen before, the PCA method produces more dimensions than the LTSA.... ..."

### Table 2. (a) Stress and (b) intrinsic dimensionality of reduced datasets

"... In PAGE 9: ... 2. Distance distribution histograms for Deviation metrics and (a) vector model, (b) FastMap The stress, summarised in Table2 a is quite low for both LSI and random projection, however in case of FastMap are the deviations not well-preserved. From the look at distance distribution histograms of original and FastMap re- duced space in Figure 2 one can observe that the distances are highly reduced.... In PAGE 9: ... The question, if the change affects only the dissimilarity threshold will be partly solved in the next section. In Table2 b, we can observe high intrinsic dimensions for both LSI vari- ants and especially for random projection, whilst the intrinsic dimension for FastMap is surprisingly low. Additional tests on real data structures are re- quired for FastMap, to verify the indexability of reduced data.... ..."

### Table 6: The per-class RI measures for various data sets for supervised dimensionality reduction.

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"... In PAGE 14: ... To illustrate this, we used the same set of data sets as in the previous section, but this time we used the centroid of the various classes as the axes of the reduced dimensionality space. The RI measures for the different classes in each one of these data sets are shown in Table6 . Note that the number of dimension in the reduced... ..."

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### Table 6: The per-class RI measures for various data sets for supervised dimensionality reduction.

2000

"... In PAGE 13: ... To illustrate this, we used the same set of data sets as in the previous section, but this time we used the centroid of the various classes as the axes of the reduced dimensionality space. The RI measures for the different classes in each one of these data sets are shown in Table6 . Note that the number of dimension in the reduced... ..."

Cited by 50

### Table 6: The per-class RI measures for various data sets for supervised dimensionality reduction.

2000

"... In PAGE 13: ... To illustrate this, we used the same set of data sets as in the previous section, but this time we used the centroid of the various classes as the axes of the reduced dimensionality space. The RI measures for the different classes in each one of these data sets are shown in Table6 . Note that the number of dimension in the reduced... ..."

Cited by 50

### Table 6: The per-class RI measures for various data sets for supervised dimensionality reduction.

2000

"... In PAGE 13: ... To illustrate this, we used the same set of data sets as in the previous section, but this time we used the centroid of the various classes as the axes of the reduced dimensionality space. The RI measures for the different classes in each one of these data sets are shown in Table6 . Note that the number of dimension in the reduced... ..."

Cited by 50