What T-SNE (t-Distributed Stochastic Neighbor Embedding) Meaning, Applications & Example
Technique for visualizing high-dimensional data.
What is T-SNE (t-Distributed Stochastic Neighbor Embedding)?
T-SNE is a dimensionality reduction technique used for visualizing high-dimensional data. It works by mapping the data into two or three dimensions, preserving the similarity between data points. T-SNE is commonly used to visualize complex datasets, like word embeddings or neural network activations, in a way that humans can interpret.
How T-SNE Works
- Pairwise Similarity: T-SNE calculates the probability of points being neighbors based on their distances in the high-dimensional space.
- Low-Dimensional Embedding: It then tries to create a lower-dimensional representation that best preserves these pairwise similarities.
Applications of T-SNE
- Data Visualization: Helps visualize high-dimensional datasets, such as clusters of customers, or words in a corpus.
- Exploratory Data Analysis: Useful for detecting patterns or anomalies in datasets that are difficult to visualize otherwise.
- Model Interpretability: Helps in understanding the structure and behavior of machine learning models by visualizing their learned representations.
Example of T-SNE
In text analysis, T-SNE can be used to visualize word embeddings generated by models like Word2Vec . By reducing the high-dimensional word vectors into two or three dimensions, T-SNE helps in seeing how words with similar meanings are grouped together, making it easier to explore the semantic relationships between words.