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.

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

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

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