Difference between revisions of "Isomap"
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* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
| − | * [[Local Linear Embedding]] | + | * [[Local Linear Embedding (LLE)]] |
* [[Kernel Approximation]] | * [[Kernel Approximation]] | ||
* [http://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] | * [http://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] | ||
Revision as of 18:23, 7 January 2019
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- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
- Local Linear Embedding (LLE)
- Kernel Approximation
- Isomap | 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.
