What are the differences between PCA and t-SNE in machine learning?

What are the differences between PCA and t-SNE in machine learning?

PCA (Principal Component Analysis) is a linear dimensionality reduction technique, while t-SNE (t-distributed Stochastic Neighbor Embedding) is a non-linear technique. PCA focuses on maximizing variance, whereas t-SNE emphasizes preserving local structure.

When should I use PCA instead of t-SNE?

Use PCA when you need a faster, linear approach for large datasets or when interpretability is important. t-SNE is better for visualizing complex, high-dimensional data.

Can you explain how t-SNE works?

t-SNE converts similarities between data points into probabilities, creating a low-dimensional representation that maintains local relationships. It uses a cost function to minimize the divergence between high-dimensional and low-dimensional distributions.

What are some common applications of PCA and t-SNE?

PCA is often used for noise reduction and feature extraction, while t-SNE is popular for visualizing clusters in high-dimensional data, such as in image and text analysis.