Kernel embedding of distributions
term_id: kernel_embedding_of_distributions
Category: training_techniques
Definition
Kernel Embedding of Distributions allows probabilistic objects to be treated as points in a high-dimensional feature space called a Reproducing Kernel Hilbert Space (RKHS). By mapping distributions to mean embeddings, complex statistical operations like computing distances between distributions or conditional expectations become linear algebra problems. This approach facilitates non-parametric statistical inference and is crucial in advanced machine learning tasks involving distributional data, such as two-sample testing and causal inference.
Summary
A technique that maps probability distributions into a reproducing kernel Hilbert space to enable comparison and manipulation via vector operations.
Key Concepts
- Reproducing Kernel Hilbert Space
- Mean Embedding
- Non-parametric Inference
- Distribution Comparison
Use Cases
- Two-sample hypothesis testing
- Causal discovery from observational data
- Comparing generative model outputs