Which of the following techniques is used to reduce the dimensionality of video data by extracting relevant features?Question 19Answera.Principal Component Analysis (PCA)b. Singular Value Decomposition (SVD)c.K-Means Clusteringd. Linear Discriminant Analysis (LDA)

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)

n unsupervised learning, which of the following methods can be used for dimensionality reduction?a)Principal Component Analysis (PCA)b)Random Forestc)AdaBoostd)Gradient Boosting

Which of the following is a method for evaluating the importance of features in a supervised learning model?Review LaterPrincipal component analysis (PCA)K-means clusteringRecursive feature eliminationLinear discriminant analysis (LDA)

Which of the following is not a data reduction technique?a.Feature creationb.samplingc.Normalizationd.Dimensionality reduction