t-SNE, which stands for t-Distributed Stochastic Neighbor Embedding, is a dimensionality reduction technique commonly used in machine learning and data visualization. It’s particularly effective at visualizing high-dimensional data in a lower-dimensional space while preserving the local structure of the data points.

Unlike some other dimensionality reduction techniques like PCA (Principal Component Analysis), t-SNE focuses on capturing the relationships between data points based on their similarities rather than on their overall variance. It does this by modeling pairwise similarities between data points in the original high-dimensional space and the lower-dimensional space, aiming to maintain these similarities as much as possible.

The t-SNE algorithm involves two main steps:

1. Computing Similarities: Initially, t-SNE calculates pairwise similarities between data points using a Gaussian distribution. Similar data points will have higher probabilities of being chosen as neighbors.
2. Mapping to Lower-Dimensional Space: The algorithm then seeks a lower-dimensional representation in such a way that the pairwise similarities in the lower-dimensional space closely match those in the high-dimensional space. It minimizes the difference between the Gaussian distribution of pairwise similarities in the original space and the Student’s t-distribution in the lower-dimensional space.

Keep in mind that while t-SNE is excellent for visualization, it’s not as interpretable as some other methods, and it can be sensitive to hyperparameters. It’s also computationally expensive and can struggle with large datasets. As for using it in bulk email communication, t-SNE itself is not directly related to email sending strategies or spam prevention. If you’re sending bulk emails and want to avoid having them marked as spam, you might want to focus on email content, formatting, and ensuring that your emails comply with spam prevention guidelines.