What are some common dimensionality reduction techniques used in machine learning?

Common dimensionality reduction techniques include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA). - **PCA** reduces dimensionality by transforming the data into a set of orthogonal (uncorrelated) components that retain the maximum variance. It’s useful for noise reduction and visualization. - **t-SNE** is primarily used for visualizing high-dimensional data in two or three dimensions by converting similarities between data points to joint probabilities. It preserves local structures well but is computationally expensive. - **LDA** works by maximizing the distance between classes while minimizing the spread within each class, making it effective for supervised dimensionality reduction. Each technique has its strengths, and the choice largely depends on the specific context and data characteristics.