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

The video tutorial covers dimensionality reduction, focusing on feature selection and extraction. It introduces PCA as a fundamental technique, explaining its theory and application. The tutorial also explores kernel PCA and related techniques like ISOMAP and LLE, emphasizing their connection to kernel PCA. The importance of mathematical foundations is highlighted, with a recommendation to review a separate module for better understanding.

What is the main difference between feature selection and feature extraction?

• Feature selection and feature extraction both retain original features.

• Feature selection and feature extraction both create new features.

• Feature selection retains original features, while feature extraction creates new features.

• Feature selection creates new features, while feature extraction retains original features.

What is the primary focus of Principal Component Analysis (PCA)?

• To reduce the dimensionality of data while retaining variance.

• To create a nonlinear transformation of data.

• To increase the number of features.

• To select the most important original features.

Kernel PCA is an extension of PCA that is:

• Feature selection-based

• Nonlinear

• Linear

• Two-dimensional

Which of the following is NOT a neighborhood technique related to kernel PCA?

• ISOMAP

• Linear Regression

• Locally Linear Embedding

• Laplacian Eigenmaps

What is the purpose of understanding PCA and kernel PCA thoroughly?

• To apply them without understanding the math.

• To focus only on feature selection.

• To understand other techniques like ISOMAP and LLE.

• To avoid using any mathematical foundations.

Why is it important to review the mathematical foundations module before studying PCA?

• To learn how to implement PCA in Python.

• To understand the derivations and concepts of PCA and kernel PCA.

• To memorize the steps of PCA.

• To avoid using any mathematical concepts.

What mathematical concepts are essential for understanding PCA and kernel PCA?

• Calculus and differential equations

• Probability and statistics

• Linear regression and classification

• Vector space, subspace, and eigen decomposition