Explore MATLAB source code curated for "流形学习算法" with clean implementations, documentation, and examples.
A significant unsupervised dimensionality reduction technique, this method serves as a linearized variant of the manifold learning algorithm Laplacian Eigenmaps, demonstrating exceptional performance in facial recognition applications. Implementation typically involves constructing adjacency graphs, computing Laplacian matrices, and solving generalized eigenvalue problems.
MATLAB implementation of the CCA manifold learning algorithm developed by Fei Sha et al., based on MVU algorithm framework with computational efficiency considerations
LLE Algorithm MATLAB Source Code - A Manifold Learning Approach for Dimensionality Reduction
The linearized method for the manifold learning algorithm LTSA (Local Tangent Space Alignment) is applied in gene classification and clustering, enabling linear projection of new samples into low-dimensional space. Implementation typically involves constructing local tangent spaces and aligning them globally through eigenvalue decomposition.
Comprehensive Collection of Manifold Learning Algorithms Featuring MDS, PCA, ISOMAP, and LLE with Implementation Insights
The linearization method for Locally Linear Embedding (LLE) is an unsupervised dimensionality reduction technique that offers a key advantage over standard LLE: it enables mapping new sample points to the low-dimensional space, which is typically implemented through an out-of-sample extension using projection matrices or kernel methods.
A manifold learning algorithm for nonlinear dimensionality reduction, originally published in Science magazine in 2000, with enhanced code implementation details and algorithm explanation
A manifold learning algorithm for nonlinear dimensionality reduction that primarily represents geometric features at each point using local tangent spaces and then aligns these local tangent spaces for global embedding.
A feature-rich manifold learning algorithm toolkit including graphical demo file (demo.fig), featuring Laplacian Eigenmaps implementation, manifold regularization adjustment, SVM classification algorithms, and other essential components for machine learning research.