APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which of the following about dimensionality reduction techniques is true?
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PCA is used for feature selection
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PCA is an unsupervised dimensionality reduction technique
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LDA is an unsupervised dimensionality reduction technique
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All principal components found by PCA are parallel to each other
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Explanation:
Detailed explanation-1: -Expert Answer All principal components are orthogonal to each other is the answer.
Detailed explanation-2: -Principal Component Analysis (PCA) Principal Component Analysis is one of the leading linear techniques of dimensionality reduction. This method performs a direct mapping of the data to a lesser dimensional space in a way that maximizes the variance of the data in the low-dimensional representation.
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