Kernel Methods on English AI Terms Dictionary

Random feature

Sat, 18 Jul 2026 10:13:36 +0000

Definition

Random feature maps transform inputs into a new space where linear models can approximate non-linear kernel functions. This approach, often associated with the Nystrom method or Fourier features, allows for scalable kernel regression and classification. By avoiding the explicit computation of large kernel matrices, it reduces computational complexity from quadratic to linear in the number of samples, making it suitable for large-scale datasets.

Summary

A technique that maps input data into a higher-dimensional space using random projections to approximate kernel methods efficiently.

Kernel embedding of distributions

Sat, 18 Jul 2026 10:03:27 +0000

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.