ISOMAP Manifold Learning MATLAB Implementation
- Login to Download
- 1 Credits
Resource Overview
Classical manifold learning algorithm ISOMAP for data preprocessing, which discovers intrinsic data structures through geodesic distance computation on neighborhood graphs. The mapped low-dimensional representation enables further operations like classification and regression. Experimental results on software engineering datasets demonstrate ISOMAP's superior performance over PCA. The implementation includes key functions for neighborhood graph construction, shortest path calculation, and multidimensional scaling.
Detailed Documentation
The classic ISOMAP manifold learning code serves as an effective preprocessing tool for high-dimensional data analysis. This MATLAB implementation constructs a neighborhood graph from input data, computes geodesic distances using Dijkstra's algorithm, and applies classical multidimensional scaling (MDS) to obtain low-dimensional embeddings. The algorithm reveals intrinsic data relationships by preserving geodesic distances in the original manifold structure, making it suitable for subsequent machine learning tasks like classification and regression. Experimental validation on software engineering datasets confirms ISOMAP's advantage over Principal Component Analysis (PCA) in capturing nonlinear structures. The code's flexibility allows application to various data types including images and text documents through appropriate distance metric customization. Key implementation components include k-nearest neighbor graph formation, Floyd-Warshall algorithm for all-pairs shortest paths, and eigenvalue decomposition for dimensionality reduction. Researchers can adapt this implementation by modifying neighborhood parameters and distance metrics for specific domain applications.
- Login to Download
- 1 Credits