Dimensionality Reduction Techniques
Dimensionality reduction techniques are methods used to reduce the number of features or variables in a dataset while retaining as much information as possible. These techniques are commonly used in data science and machine learning to address the curse of dimensionality, which refers to the difficulty of analyzing and modeling high-dimensional data. Dimensionality reduction techniques can be divided into two categories: feature selection and feature extraction. Feature selection involves selecting a subset of the original features based on some criteria, while feature extraction involves transforming the original features into a lower-dimensional space. Some common dimensionality reduction techniques include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-SNE, and Autoencoders.
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