Answer / Sumit Kumar Watas

Dimensionality reduction techniques aim to transform high-dimensional data into a lower-dimensional space while preserving essential information. Some common methods include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Non-negative Matrix Factorization (NMF). PCA finds the linear combination of features that capture the maximum variance in the data, LDA aims to find directions that maximize the separability between classes while minimizing within-class variance, and NMF seeks non-negative factors that explain the original data as a linear sum of non-overlapping basis elements.

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