Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Criteria

The video tutorial explores the concept of finding a subspace that best represents data, focusing on two main criteria: minimizing reconstruction error and maximizing variance. Through a simple example of two-dimensional data, the tutorial explains how to project data onto a one-dimensional subspace and reconstruct it. The importance of variance as a measure of information retention is highlighted, and the connection between variance and reconstruction error is discussed. The tutorial concludes with a brief overview of the criteria for Principal Component Analysis (PCA) and a preview of the next video.

Describe how the direction of projection affects the variance of the data.

What are the two criteria for determining the best subspace for data representation?

In your own words, explain the significance of variance in data representation.