T-SNE
t-SNE is a nonlinear dimensionality-reduction technique that maps high-dimensional data to a lower-dimensional space while emphasizing local similarities or neighborhood relationships.
Data Without Labels
t-distributed stochastic neighbor embedding is a nonlinear dimensionality-reduction technique that maps high-dimensional observations to a low-dimensional space while preserving local similarities.
- How it is used
- Visualizes local similarities in high-dimensional data, including image measurements, through low-dimensional projections.
- How deep it goes
- Traces Gaussian and Cauchy similarities through KL-divergence minimization, then demonstrates fitting and plotting in Python.
- Examples
- Uses labeled Iris scatterplots and reshaped MNIST digit images to inspect two-dimensional projections.
- What you can do after
- Apply it via scikit-learn’s TSNE to create and interpret labeled two-dimensional plots, while checking perplexity choices.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
t-SNE is a nonlinear dimensionality-reduction technique that maps high-dimensional data to a low-dimensional space while emphasizing local neighborhood structure.
- How it is used
- Supports exploratory visualization of complex tabular data by exposing patterns, clusters, and possible anomalies.
- How deep it goes
- Reduces numeric multivariate data to plotted dimensions, but stops short of stable interpretation because hyperparameters alter projections.
- Examples
- Uses numeric and ordinal dataset features, mean imputation, scikit-learn TSNE, and a two-dimensional scatter plot.
- What you can do after
- Apply it via scikit-learn's TSNE to create and plot a two-dimensional data projection.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains t-SNE →
Related concepts
- dimensionality reductionkind of · Dimensionality reduction is the transformation of data into a representation with fewer dimensions while retaining selected information, structure, or relationships.
- embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- Kullback–Leibler divergenceuses · Kullback–Leibler divergence is a nonnegative, directed measure of the information lost when one probability distribution approximates another, computed as an expected log ratio under the first distribution.
- UMAPalternative to · UMAP, or Uniform Manifold Approximation and Projection, is a nonlinear manifold-learning technique that embeds high-dimensional data in a lower-dimensional space while preserving important neighborhood and structural relationships.
- principal component analysisalternative to · Principal component analysis is a linear dimensionality-reduction technique that transforms data into orthogonal components ordered by the variance they capture.
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