Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Supervised PCA and Fishers Linear D

The video tutorial discusses supervised feature extraction techniques, focusing on supervised PCA and Fisher's Linear Discriminant Analysis (LDA). It explains how supervised PCA uses label information for feature selection before applying PCA, enhancing classification or regression tasks. Fisher's LDA is detailed, emphasizing maximizing between-class variance and minimizing within-class variance. The video also covers LDA's applications, including person reidentification in computer vision, and concludes with a preview of the next topic on feature engineering.

What is a key limitation of PCA in supervised learning tasks?

• It requires a large amount of data.

• It does not utilize label information.

• It is computationally expensive.

• It only works with binary classification.

What is the first step in the supervised PCA process?

• Normalizing the data.

• Applying standard PCA.

• Performing feature selection with label information.

• Using a wrapper method.

Which method is commonly used in the first phase of supervised PCA?

• Random selection.

• Filter method.

• Backward elimination.

• Wrapper method.

What is the main goal of Fisher's Linear Discriminant Analysis?

• To increase the number of features.

• To maximize between-class variance and minimize within-class variance.

• To eliminate outliers.

• To reduce computational complexity.

In LDA, what does maximizing the difference between projected means achieve?

• It reduces the dimensionality of the data.

• It ensures data points within a class are compact.

• It separates different classes as much as possible.

• It increases the number of classes.

What mathematical technique is used in the solution of the Fisher criterion?

• Gradient descent.

• Linear regression.

• Eigenvalue decomposition.

• Fourier transform.

In the context of LDA, what is meant by 'within-class variance'?

• Variance of the noise in the data.

• Variance of the entire dataset.

• Variance within a single class.

• Variance between different classes.

Which of the following is a variant of LDA mentioned in the video?

• Principal Component Analysis.

• K-means Clustering.

• Support Vector Machine.

• Null Folly Summon Transform.

What is a reported application of the Null Folly Summon Transform?

• Image compression.

• Person reidentification.

• Speech recognition.

• Weather prediction.

What is the primary benefit of using supervised feature extraction techniques?

• They increase the speed of model training.

• They improve class discrimination by using label information.

• They enhance the interpretability of the model.

• They reduce the need for data preprocessing.