Explore MATLAB source code curated for "lda" with clean implementations, documentation, and examples.
MATLAB source code for LDA implementation developed by Jonathan Huang - the only currently available and verified correct implementation suitable for image feature dimensionality reduction applications
A classification program combining Linear Discriminant Analysis and Support Vector Machine algorithms
After extensive searching, I found a robust LDA (Latent Dirichlet Allocation) implementation featuring optimized code structure and comprehensive documentation - highly recommended for experimentation!
High-quality LDA (Linear Discriminant Analysis) MATLAB implementation featuring efficient large dataset handling with optimized matrix operations and dimensionality reduction techniques.
Common manifold learning methods LDA and LLE - This program has been debugged and is fully functional! Includes detailed algorithm explanations and implementation insights.
Linear Discriminant Analysis (LDA) is a dimensionality reduction technique that projects data into a subspace optimized for class separability. The algorithm identifies basis vectors that maximize inter-class variance while minimizing intra-class variance within the projected subspace. Implementation typically involves computing scatter matrices and solving a generalized eigenvalue problem to determine the optimal projection axes.
MATLAB code implementation of LDA for topic modeling, featuring Gibbs sampling and text mining applications with data preprocessing and visualization tools.
Face Feature Extraction Using LDA and PCA Pattern Recognition Methods with Code Implementation Details
MATLAB code implementation for dimensionality reduction with PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), including integration with KNN classification for pattern recognition applications.