Explore MATLAB source code curated for "lpp" with clean implementations, documentation, and examples.
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.
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.
LPP is a highly effective tool for data dimensionality reduction, offering practical applications in various data processing scenarios. It is implemented through an algorithm that preserves local data structures while reducing computational complexity.
Performance and Stability Testing for LPP-Based Facial Recognition Systems
Comparative Analysis of PCA, LDA, and LPP for Feature Dimensionality Reduction in Face Recognition Systems
LPP for Data Dimensionality Reduction with Code Implementation Insights
Introduction to Locality Preserving Projections (LPP) as a nonlinear dimensionality reduction technique, with algorithm principles and implementation insights