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

The video tutorial covers the implementation of Principal Component Analysis (PCA) using Numpy. It begins with setting up the environment and importing necessary libraries. The instructor then loads a face dataset and explains how to prepare the data by calculating the mean vector and covariance matrix. The tutorial proceeds with eigen decomposition to find eigenvalues and eigenvectors, which are used for dimensionality reduction. The instructor demonstrates how to reconstruct data from reduced dimensions and visualizes eigenfaces. The video concludes with a discussion on choosing the optimal number of dimensions (K) to retain most of the data's information.

Which library is primarily used for numerical computations in the PCA implementation?

• Pandas

• Numpy

• TensorFlow

• Matplotlib

What is the dimensionality of each face image in the dataset?

• 62x47

• 100x100

• 50x50

• 80x80

What is the purpose of computing the mean face in PCA?

• To visualize the average face

• To reduce noise

• To increase dimensionality

• To sort eigenvalues

What is the shape of the covariance matrix in this PCA implementation?

• 2914x2914

• 1140x1140

• 62x47

• 100x100

Why is it important to sort eigenvalues in descending order?

• To identify the most important dimensions

• To reduce computation time

• To increase the number of dimensions

• To visualize the data

What happens to the data when dimensions are reduced using PCA?

• It retains essential information

• It becomes unrecognizable

• It loses all information

• It becomes more complex

What is an eigenface?

• A face with maximum variance

• A face constructed from eigenvectors

• A face with no variance

• A face with minimum variance

How can the optimal number of dimensions (K) be determined in PCA?

• By reducing to a single dimension

• By using all dimensions

• By retaining a certain percentage of information

• By trial and error

What is a potential criticism of PCA?

• It always increases noise

• It may discard important discriminative information

• It requires too much data

• It is too complex to implement

What percentage of information is often considered sufficient to retain in PCA?

• 50%

• 100%

• 95%

• 70%