Glossary
T-SNE (t-Distributed Stochastic Neighbor Embedding)
T-SNE, or t-Distributed Stochastic Neighbor Embedding, is a popular algorithm used for data visualization. It is particularly useful in displaying high-dimensional data in a lower-dimensional space, making it easier to understand and interpret.
The main purpose of T-SNE is to preserve the local structure of the data while also highlighting the global structure. It achieves this by converting the similarities between data points into probabilities. These probabilities are then used to create a map or plot where similar data points are positioned close to each other, while dissimilar points are placed further apart.
One of the key advantages of T-SNE is its ability to reveal clusters or groups within complex datasets. This can be extremely helpful when analyzing large datasets with numerous variables, as it allows for easy identification of patterns and relationships that may not be immediately apparent in the original data.
T-SNE works by iteratively calculating the pairwise similarities between data points. It then tries to find a lower-dimensional representation that minimizes the divergence between these pairwise similarities in the original high-dimensional space and the lower-dimensional space.
However, it is important to note that T-SNE has certain limitations. It is a non-linear algorithm, which means that it can distort the distances between points. Additionally, the results may vary depending on the chosen hyperparameters, such as the perplexity and learning rate.
In conclusion, T-SNE is a powerful tool for visualizing high-dimensional data. It allows for the identification of patterns and clusters that may be hidden in the original dataset. While it has its limitations, when used correctly and with careful consideration of its parameters, T-SNE can provide valuable insights into complex data structures.
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