Laplacian eigenmaps
Laplacian eigenmaps is a dimensionality reduction technique used in machine learning and data analysis, particularly for nonlinear data structures.
data analysis
Principal component analysis (PCA)
Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of data by transforming it into a new set of variables called principal components. These components capture the maximum variance within the data, enabling easier visualization, interpretation, and noise reduction.
Dimensionality reduction
Dimensionality reduction is a process in data analysis and machine learning that transforms data from a high-dimensional space into a lower-dimensional space while preserving essential properties. It facilitates visualization, reduces storage requirements, and helps improve the performance of algorithms by eliminating redundant or irrelevant features.
OPTICS algorithm
The OPTICS (Ordering Points To Identify the Clustering Structure) algorithm is a density-based clustering method that identifies clusters of varying shapes and sizes in large datasets.
Causal tracing
Causal tracing is a method used to identify and analyze the cause-and-effect relationships within complex systems.