Understanding PCA Concepts

The PCA in data analysis stand for

• Principal Component Algorithm

• Primary Component Analysis

• Principal Coordinate Analysis

• Principal Component Analysis

The primary purpose of PCA is

• To visualize data in three dimensions.

• The primary purpose of PCA is to reduce the dimensionality of data.

• To eliminate outliers from the dataset.

• To increase the dimensionality of data.

How does PCA reduce the dimensionality of data?

• PCA reduces dimensionality by projecting data onto principal components that capture the most variance.

• PCA clusters data points into distinct groups without reducing dimensions.

• PCA reduces dimensionality by removing all features equally.

• PCA increases dimensionality by adding new features.

The eigenvalue in the context of PCA

• Eigenvalues represent the number of principal components.

• Eigenvalues indicate the direction of the data points.

• Eigenvalues are the coefficients of the original variables.

• Eigenvalues in PCA indicate the variance explained by each principal component.

The eigenvectors in PCA

• Eigenvectors indicate the directions of maximum variance in PCA.

• Eigenvectors represent the data points in PCA.

• Eigenvectors determine the number of principal components in PCA.

• Eigenvectors are used to calculate the mean of the data.

Image Compression in Computer Vision
You have 1024-pixel grayscale images stored as feature vectors. An AI engineer applies PCA and keeps only 50 principal components. The main advantage is

• Faster model training with reduced storage requirements

• Increase in image sharpness

• Elimination of all noise from images

• Conversion of images to binary format

IoT Sensor Data in Manufacturing
A factory collects 200 sensor readings every second. PCA is applied before anomaly detection. The primary benefit is

• Reduce redundant data and focus on major variance patterns

• Increase the number of sensors virtually

• Eliminate all faulty sensor readings

• Make the data binary for faster processing

Marketing Analytics
A retail company has 60 customer metrics (age, spend patterns, preferences). After PCA, only 3 components are used for visualization. The purpose is

• To identify and visualize patterns in a lower-dimensional space

• To remove all irrelevant customers

• To increase data dimensionality for better clustering

• To guarantee higher customer retention

Biomedical Signal Processing
EEG recordings have thousands of time-series points per patient. PCA is applied before feeding data to a neural network. What’s the main advantage?

• Minimize irrelevant noise and reduce computational cost

• Guarantee 100% disease detection accuracy

• Convert EEG signals into images

• Increase the amplitude of EEG signals

Cybersecurity Network Traffic Analysis
Network logs have hundreds of features per connection. PCA is used before training an intrusion detection system. Why?

• Reduce dimensionality to speed up model training and avoid overfitting

• Encrypt the network traffic automatically

• Ensure all connections are safe

• Increase the variance artificially