COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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1 and 2
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2 and 3
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1 and 3
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1, 2 and 3
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Detailed explanation-1: -In general, PCA works best if there is a linear structure to the data. It works poorly if the data lies on a curved surface and not on a flat surface.
Detailed explanation-2: -Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.
Detailed explanation-3: -PCA works best on data sets having 3 or higher dimensions. Because, with higher dimensions, it becomes increasingly difficult to make interpretations from the resultant data cloud.
Detailed explanation-4: -But, as we know, PCA does not work well for non-linear data. On the other hand, autoencoders can model non-linear data. Therefore, before data compression, it is essential to know if the data is linear or not.