Advanced Dimensionality Reduction Techniques

When working with high-dimensional data, reducing dimensionality can help you uncover patterns, speed up computation, and improve model performance. In this lesson, we’ll explore two popular techniques—t-SNE and autoencoders. Let’s get started.

t-SNE for data visualization

You’re working with a high-dimensional dataset (e.g., images, embeddings, or genomics data) and need to visualize the relationships between points. The interviewer asks you to explain t-distributed stochastic neighbor embedding (t-SNE) and how it compares to PCA.

Sample answer

t-SNE is a dimensionality reduction technique primarily designed for visualization purposes rather than general feature reduction. It works by converting high-dimensional data into a probability distribution where similar points have high probabilities of being selected as neighbors. Then, it constructs a similar probability distribution in the low-dimensional space and minimizes the Kullback-Leibler divergence between these distributions.

This technique is particularly well-suited for visualizing high-dimensional data by reducing it to two or three dimensions. Unlike other methods, t-SNE focuses on preserving the local structure of the data, making it ...