t-SNE (t-Distributed Stochastic Neighbor Embedding)

Overview:

• t-SNE is a non-linear dimensionality reduction technique primarily used for visualization.

• It converts high-dimensional Euclidean distances into conditional probabilities that represent similarities.

• The algorithm minimizes the Kullback-Leibler divergence between these probability distributions in the high-dimensional and low-dimensional space.

Key Characteristics:

• Effective at creating a visual representation of complex data, revealing clusters and patterns.

• Sensitive to parameters such as perplexity and learning rate.

• Computationally intensive, especially for large datasets.

Applications:

• Visualizing high-dimensional data such as images, text, and gene expression data.

• Exploring and understanding the structure of the data.