Difference between revisions of "Isomap"
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* [[Dimensional Reduction]] | * [[Dimensional Reduction]] | ||
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
| − | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | + | * [[Embedding]] |
| − | * [[Local Linear Embedding (LLE)]] | + | ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] |
| + | ** [[Local Linear Embedding (LLE)]] | ||
* [[Kernel Trick]] | * [[Kernel Trick]] | ||
* [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] | * [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] | ||
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* [https://science.sciencemag.org/content/295/5552/7 The Isomap Algorithm and Topological Stability | M. Balasubramanian, E. Schwartz, J. Tenenbaum, Vin de Silva and J. Langford] | * [https://science.sciencemag.org/content/295/5552/7 The Isomap Algorithm and Topological Stability | M. Balasubramanian, E. Schwartz, J. Tenenbaum, Vin de Silva and J. Langford] | ||
| − | a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods.[1] Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities. | + | a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional [[embedding]] methods.[1] Isomap is used for computing a quasi-isometric, low-dimensional [[embedding]] of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities. |
https://science.sciencemag.org/content/sci/295/5552/7/F1.medium.gif | https://science.sciencemag.org/content/sci/295/5552/7/F1.medium.gif | ||
Revision as of 20:14, 26 June 2023
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- Principal Component Analysis (PCA)
- Embedding
- Kernel Trick
- Isomap | Wikipedia
- Nonlinear dimensionality reduction | Wikipedia
- The Isomap Algorithm and Topological Stability | M. Balasubramanian, E. Schwartz, J. Tenenbaum, Vin de Silva and J. Langford
a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods.[1] Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities.
