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/** @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. | |
* | |
*/ |
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