Difference between revisions of "Isomap"
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* [[...find outliers]] | * [[...find outliers]] | ||
* [[Anomaly Detection]] | * [[Anomaly Detection]] | ||
| − | * [[Dimensional Reduction | + | * [[Dimensional Reduction]] |
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
Revision as of 21:59, 27 June 2019
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.
