PCA vs t-SNE (Dimensionality Reduction techniques)

PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are both dimensionality reduction techniques that can be used in machine learning and data analysis.

PCA is a linear transformation method that identifies the most important features (principal components) that explain the most variance in the data. It reduces the number of features in the data, making it easier to visualize and analyze. It works well for high-dimensional data and is computationally efficient. However, PCA assumes that the data is…