An Introduction to Locally Linear Embedding
By Lawrence K. Saul, Sam T. Roweis. 2001
318 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/da6495102377f8332b21cef50c35d27fbff627df
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Citations in the corpus
- Diffusion Kernels on Graphs and Other Discrete Input Spaces (2002, methodology): “Finally, adjacency graphs are sometimes used when data is expected to be confined to a manifold of lower dimensionality than the original space (Saul & Roweis, 2001; Belkin & Niyogi, 2001) and (Szummer & Jaakkola, 2002).”
- Diffusion kernels on graphs and other discrete structures (2002, methodology): “Finally, adjacency graphs are sometimes used when data is expected to be confined to a manifold of lower dimensionality than the original space (Saul & Roweis, 2001; Belkin & Niyogi, 2001) and (Szummer & Jaakkola, 2002).”
- Overview and comparative study of dimensionality reduction techniques for high dimensional data (2020, unknown): “LLE uses eigen vector method for nonlinear DR process and can successfully work for the identification of manifold structures [219] .”
- Distance Metric Learning with Application to Clustering with Side-Information (2002, methodology): “This includes algorithms such as Multidimensional Scaling (MDS) [2], and Locally Linear Embedding (LLE) [9].”
- Locally Linear Embedding for Exemplar-Based Spectral Conversion (2016, background): “The detailed derivations of the solution can be found in [23].”
- Exploiting manifold geometry in hyperspectral imagery (2005, background): “Recently, a number of papers that address the problem of modeling nonlinear data structure have appeared in the statistical pattern recognition literature [1], [2], [21], [33], [35], [ 36 ], [42].”
- Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) (2021, unknown): “, Nonlinear dimensionality reduction by locally linear embedding, Science, 290, 5500, 2323-2326 (2000) [42] Saul, L.”