Navigating High-Dimensional Spaces with Dimensionality Reduction

Photo by Shubham Dhage on Unsplash

Dimensionality reduction is a technique used in machine learning and statistics to reduce the number of input variables or features in a dataset. High-dimensional datasets, where the number of features is large, can suffer from the curse of dimensionality, leading to increased computational complexity, overfitting, and difficulty in visualization. Dimensionality reduction methods aim to overcome these challenges by extracting the most important information from the data while discarding less relevant features. Here are key aspects of dimensionality reduction:

I. Techniques for Dimensionality Reduction:

1. Principal Component Analysis (PCA):

• PCA is a linear technique that transforms the original features into a new set of uncorrelated variables, the principal components.
• Principal components capture the maximum variance in the data, and the first few components often contain the most significant information.

2. t-Distributed Stochastic Neighbor Embedding (t-SNE):

• t-SNE is a nonlinear technique primarily used for visualization.
• It focuses on preserving the pairwise similarities between data…