Difference between revisions of "Local Linear Embedding (LLE)"
| Line 15: | Line 15: | ||
* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
* [[Isomap]] | * [[Isomap]] | ||
| − | * [[Kernel | + | * [[Kernel Trick]] |
* [http://cs.nyu.edu/~roweis/lle/ Locally Linear Embedding | S.T. Roweis & L. K. Saul - NYU] | * [http://cs.nyu.edu/~roweis/lle/ Locally Linear Embedding | S.T. Roweis & L. K. Saul - NYU] | ||
* [http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear dimensionality reduction | Wikipedia] | * [http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear dimensionality reduction | Wikipedia] | ||
Revision as of 11:38, 22 June 2019
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- AI Solver
- ...find outliers
- Anomaly Detection
- Dimensional Reduction Algorithms
- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
- Isomap
- Kernel Trick
- Locally Linear Embedding | S.T. Roweis & L. K. Saul - NYU
- Nonlinear dimensionality reduction | Wikipedia
begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describes the point as a linear combination of its neighbors. Finally, it uses an [1]-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model. LLE was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems
