Which of the following techniques is used for dimensionality reduction while preserving the data's variance?
Principal component analysis, or PCA, is a dimensionality reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Statements:
A < B ≤ T < Y = O; V < R > K ≥ F > O
Conclusions:
I). B < R
II). Y ≥ K
...Statements: R < S > T; U < V ≤ S; R > P
Conclusions:
I. S > P
II. U < R
III. T < P
Statements: E > G < H > F = I ≥ J > K ≥ D
Conclusion
I: J < H
II: I > D
Statements:S > T,T ≥ U,U < V
Conclusions: I. T > V II. S > U
Statements: O ≥ M > F, K ≤ J ≤ D = F, B ≤ Z ≤ L = K
Conclusion:
I. M > L
II. D ≥ B
Statement: L > J ≥ U ≥ F; P < S < L
Conclusion: I. S < F II. P < U
Statements: F > G ≥ H; I ≥ J < H; J > K > L
Conclusions:
I. F > L
II. H ≥ K
III. G ≥ J
Statements: R © K, K * N, N $ J, J % H
Conclusions: Â Â Â Â I.R $ NÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â II.J @ KÂ Â Â Â Â Â Â Â Â Â Â Â Â...
Which of the following symbols should be placed in the blank spaces (_) respectively (in the same order from left to right) to complete the given expre...
Statement: C ≥ D > E < F ≥ G; D ≥ H = J
Conclusion:
I. C > H
II. C = J
