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

The video tutorial discusses Principal Component Analysis (PCA), a powerful technique for dimensionality reduction. It explains how PCA generates new features that are highly representative and does not require label information, making it an unsupervised method. The tutorial provides an example of reducing data from a three-dimensional space to a two-dimensional space, emphasizing the importance of selecting the best subspace to minimize information loss. The video concludes with a preview of the next topic, which will cover the criteria PCA uses to retain maximum information while reducing dimensions.

What is the significance of the basis vectors in the context of PCA?

Discuss the implications of reducing dimensions in PCA. What information might be lost?

How can we formalize the concept of retaining information while reducing dimensions in PCA?