Which method can be used for dimensionality reduction in machine learning?

Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction in machine learning. It is particularly useful when dealing with large datasets that have many features (dimensions). The primary goal of PCA is to transform the data into a new coordinate system where the greatest variances in the data lie along the first few coordinates (called principal components). This transformation helps capture the most important information while reducing the number of features, thus simplifying the dataset while retaining as much variability as possible.

The process of PCA involves several steps, including centering the data, computing the covariance matrix, and then identifying the eigenvalues and eigenvectors of the covariance matrix. By selecting the top k eigenvectors corresponding to the largest eigenvalues, PCA effectively reduces the dimensionality of the dataset while maintaining the critical structure and trends present in the original data.

This is distinct from other methods listed. While linear regression is primarily used for predictive modeling, decision trees are a form of supervised learning for classification and regression tasks, and K-means clustering is an unsupervised learning method for grouping data based on feature similarity. None of these methods focus specifically on reducing the number of dimensions or features in the data as PCA does.