Explore MATLAB source code curated for "高维数据" with clean implementations, documentation, and examples.
This second incremental PCA algorithm enables extraction of principal components from high-dimensional data through incremental updates, featuring efficient covariance matrix maintenance through streaming data processing techniques.
Locality Preserving Projections (LPP) as a nonlinear dimensionality reduction method can be effectively applied to machine learning with high-dimensional datasets using graph-based neighborhood preservation techniques.
Principal Component Analysis (PCA) is a dimensionality reduction technique based on the Karhunen-Loève (K-L) transform. The PCA algorithm identifies an optimal linear transformation matrix W according to specific performance criteria, enabling effective reduction of high-dimensional data while preserving maximum variance.
A MATLAB function designed for calculating linear graph embedding of high-dimensional data, essential for implementing nearly all linear dimensionality reduction algorithms including LPP, NPE, IsoProjection, and LSDA. This shared resource provides core functionality for transforming high-dimensional datasets into lower-dimensional representations through graph-based linear projection methods.
Nonlinear dimensionality reduction techniques can be applied to machine learning tasks involving high-dimensional data analysis and visualization
CLIQUE is a clustering algorithm for high-dimensional data spaces that combines grid-based and density-based approaches to efficiently handle dimensionality challenges.
Introduction to Locality Preserving Projections (LPP) as a nonlinear dimensionality reduction technique, with algorithm principles and implementation insights