Which of the given options is NOT associated with the properties of the Principal Component?Select an option Clear ResponseThe Principal Components are orthogonal.The variation in the Principal Components increases from the 1st first Principal Component to the last one.The linear combinations of the original variables from the Principal ComponentsThe Principal Components helps in dimensionality reduction without information loss.

In principal component analysis, the projected lower dimensional space corresponds to – A. subset of the original co-ordinate axis B. eigenvectors of the data covariance matrix C. eigenvectors of the data distance matrix D. orthogonal vectors to the original co-ordinate axis

Which dimensionality reduction technique is affected by the curse of dimensionality?Review LaterPrincipal Component Analysis (PCA)UMAPt-SNENone of the above

Identify whether true or false:  In PCA the number of input dimensions is equal to principal components.

How does the term "principal component analysis" (PCA) contribute to dimensionality reduction in data analysis?By clustering similar data points togetherBy transforming features into a lower-dimensional spaceBy creating new features based on existing onesBy evaluating feature importance

Which of the following is an example of a dimensionality reduction technique?Question 6Answera.Support vector machine (SVM)b.Principal component analysis (PCA)c.AdaBoostd.K-nearest neighbors (KNN)