Dimensionality Reduction

Data dimensionality refers to the number of features or attributes in a dataset.

Feature dimensionality refers to the number of input variables or features in a dataset used for analysis or modeling.

Feature Projection is a technique for reducing data dimensionality in AI models, focusing on relevant features.

A Graph Laplacian Eigenmap is a technique for dimensionality reduction using graph theory.

Kernel PCA is a technique for non-linear dimensionality reduction using kernel methods.

Locally Linear Embedding (LLE) is a technique for dimensionality reduction that preserves local structure in data.

Low-dimensional space refers to a simplified representation of data in fewer dimensions, aiding in analysis and visualization.

The Manifold Hypothesis suggests that high-dimensional data can be modeled as low-dimensional surfaces in a higher-dimensional space.

Manifold learning is a type of machine learning that reduces data dimensions while preserving its structure.

Multi-Dimensional Scaling (MDS) is a statistical technique used for visualizing the similarity or dissimilarity of data points.

UMAP is a machine learning technique for visualizing high-dimensional data in lower dimensions.