Difference between revisions of "Isomap"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search] |
* [[AI Solver]] | * [[AI Solver]] | ||
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* [[Local Linear Embedding (LLE)]] | * [[Local Linear Embedding (LLE)]] | ||
* [[Kernel Trick]] | * [[Kernel Trick]] | ||
| − | * [ | + | * [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] |
| − | * [ | + | * [https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear dimensionality reduction | Wikipedia] |
| − | * [ | + | * [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 | |
Revision as of 20:26, 27 March 2023
YouTube search... ...Google search
- AI Solver
- ...find outliers
- Anomaly Detection
- Dimensional Reduction
- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
- Local Linear Embedding (LLE)
- 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.
