gistfile1.txt

/** @brief the class LaplacianEigenmaps used to preprocess
 * data using Laplacian Eigenmaps algorithm as described in:
 *
 * Belkin, M., & Niyogi, P. (2002). 
 * Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. 
 * Science, 14, 585-591. MIT Press. 
 * Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.9400&rep=rep1&type=pdf
 *
 * Note that the algorithm is very sensitive to the heat distribution coefficient
 * and number of neighbors in the nearest neighbor graph. No connectivity check
 * is provided, so the preprocessor will not produce reasonable embeddings if the
 * K value makes a graph that is not connected.
 *
 * This implementation is not parallel due to performance issues. Generalized 
 * eigenproblem is the bottleneck for this algorithm.
 *
 * Solving of generalized eigenproblem involves LAPACK DSYGVX routine
 * and requires extra memory for right-hand side matrix storage. 
 * If ARPACK is available then DSAUPD/DSEUPD is used with no extra 
 * memory usage. 
 *
 */