Data Science and Machine Learning (Theory and Projects) A to Z - Dimensionality Reduction: The Principal Component Analy

The video tutorial discusses dimensionality reduction, focusing on Principal Component Analysis (PCA) as an unsupervised learning technique. It explains the challenges of high-dimensional data and the benefits of reducing dimensions, such as increased stability and reduced computational complexity. The tutorial contrasts unsupervised and supervised dimensionality reduction, using PCA and Fisher's Linear Discriminant (FLD) as examples. An illustrative example with image data demonstrates how PCA identifies subspaces to compress data without losing information. The video concludes with an overview of dimensionality reduction's role in machine learning and hints at future topics like deep learning.

What is one of the main challenges when dealing with high-dimensional data?

• It increases the demand for more training data.

• It requires less computational power.

• It reduces the number of features automatically.

• It simplifies the data analysis process.

What is the benefit of reducing dimensions in data?

• It increases the number of data samples.

• It makes the data more complex.

• It stabilizes subsequent probabilistic models.

• It eliminates the need for data labels.

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

• To increase the number of features.

• To reduce the number of samples.

• To reduce the number of features while preserving data structure.

• To use class labels for better classification.

How does PCA handle image data for dimensionality reduction?

• By converting images into a single pixel.

• By representing images as points in a high-dimensional space.

• By using color information to reduce dimensions.

• By increasing the resolution of images.

In PCA, what happens to the data after projecting it to a subspace?

• The data can be reconstructed without information loss.

• The data becomes more complex.

• The data cannot be reconstructed.

• The data loses all its information.

What is a key difference between PCA and supervised dimensionality reduction techniques?

• Supervised techniques do not use class labels.

• Supervised techniques focus on data compression.

• PCA uses class labels for better accuracy.

• PCA focuses on data compression and reconstruction.

Which of the following is a characteristic of supervised dimensionality reduction techniques?

• They do not use class labels.

• They are always unsupervised.

• They aim to increase variation among different classes.

• They focus on data compression and reconstruction.