@article {ghojogh2021softimp,
	title = {Generative locally linear embedding: A module for manifold unfolding and visualization},
	journal = {Software Impacts},
	volume = {9},
	number = {100105},
	year = {2021},
	pages = {3},
	publisher = {Elsevier},
	abstract = {Data often have nonlinear patterns in machine learning. One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction. Locally Linear Embedding (LLE) is a nonlinear spectral method for dimensionality reduction and manifold unfolding. It embeds data using the same linear reconstruction weights as in the input space. In this paper, we propose an open source module which not only implements LLE, but also includes implementations of two generative LLE algorithms whose linear reconstruction phases are stochastic. Using this module, one can generate as many manifold unfoldings as desired for data visualization or feature extraction.},
	author = {Ghojogh, Benyamin and Ghodsi, Ali and Karray, Fakhri and Crowley, Mark}
}
