COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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PCA will perform outstandingly
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PCA will perform badly
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Can’t Say
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None of above
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Detailed explanation-1: -8. What will happen when eigenvalues are roughly equal while applying PCA? While applying the PCA algorithm, If we get all eigenvectors the same, then the algorithm won’t be able to select the Principal Components because in such cases, all the Principal Components are equal.
Detailed explanation-2: -If we don’t rotate the components, the effect of PCA will diminish and we’ll have to select more number of components to explain variance in the training set.
Detailed explanation-3: -When a given data set is not linearly distributed but might be arranged along with non-orthogonal axes or well described by a geometric parameter, PCA could fail to represent and recover original data from projected variables.
Detailed explanation-4: -Drawbacks of PCA (Principal Component Analysis) PCA is also sensitive to outliers. Such data inputs could produce results that are very much off the correct projection of the data [6]. PCA presents limitations when it comes to interpretability. Since we’re transforming the data, features lose their original meaning.
Detailed explanation-5: -In the Minimum Error Formulation, PCA is defined as the linear projection that minimises the average projection cost (mean squared error) between the data points and their projection .