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