What best describes the t-SNE dimensionality reduction technique?

t-SNE (t-distributed Stochastic Neighbor Embedding) is a popular technique used for dimensionality reduction. 🌐 Unlike some methods that aim to preserve the global structure of the data (option a), t-SNE primarily focuses on maintaining the local similarities between data points (option c). 🌟 This means that it seeks to keep similar data points close together in the reduced dimensional space, making it particularly useful for visualizing high-dimensional data.

t-SNE does not reduce dimensionality based on class labels (option b), as it is an unsupervised learning technique and does not take class labels into account. Additionally, it does not involve feature extraction using neural networks (option d); instead, it employs a probabilistic approach to model the relationships between data points.