-
-
Save serialhex/7a8015ea2d7c520aa880 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
/** @brief the class LocallyLinearEmbedding used to preprocess | |
* data using Locally Linear Embedding algorithm described in | |
* | |
* Saul, L. K., Ave, P., Park, F., & Roweis, S. T. (2001). | |
* An Introduction to Locally Linear Embedding. Available from, 290(5500), 2323-2326. | |
* Retrieved from: | |
* http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123.7319&rep=rep1&type=pdf | |
* | |
* The process of finding nearest neighbors involves Fibonacci Heap | |
* and Euclidian distance. Note: it is still not parallel. | |
* | |
* Linear reconstruction step runs in parallel for objects and | |
* involves LAPACK routine DPOSV for solving a system of linear equations. | |
* | |
* The eigenproblem stated in the algorithm is solved with LAPACK routine | |
* DSYEVR or with ARPACK DSAUPD/DSEUPD routines if available. | |
* | |
* Due to computation speed, ARPACK is being used with small | |
* regularization of weight matrix and Cholesky factorization is used | |
* internally for Lanzcos iterations. If the results aren't reasonable | |
* LUP factorization could be used with posdef parameter set to | |
* false using set_posdef. | |
* | |
*/ |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment