What are the applications of t-SNE in data analysis?

What are the applications of t-SNE in data analysis?

t-SNE, or t-distributed Stochastic Neighbor Embedding, is primarily used for visualizing high-dimensional data. It helps in reducing dimensions while preserving the structure of the data, making it easier to identify patterns and clusters.

Can you give me some specific examples of where t-SNE is used?

Sure! t-SNE is commonly used in fields like genomics for visualizing gene expression data, in image processing for clustering similar images, and in natural language processing for visualizing word embeddings.

How does t-SNE compare to other dimensionality reduction techniques?

t-SNE is particularly effective for visualizing complex datasets, but it can be slower than techniques like PCA. Unlike PCA, t-SNE focuses on preserving local structures, making it better for clustering.

Are there any limitations to using t-SNE?

Yes, t-SNE can be sensitive to parameter settings, such as perplexity, and it may not preserve global structures well. Additionally, it can be computationally intensive for very large datasets.