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 the significance of the Frobenius Norm in Fisher's linear discriminant analysis?

Discuss the importance of compactness within classes in supervised learning.

What are some common variants of linear discriminant analysis used in supervised learning?

How does the null folium transform relate to Fisher's linear discriminant analysis?