Isometric Mapping Algorithm
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An Isometric Mapping Algorithm is a nonlinear dimensionality reduction algorithm that ...
- AKA: Isomap.
- See: Manifold Learning, MDA, Kernel PCA.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Isomap Retrieved:2015-2-6.
- Isomap is a Nonlinear dimensionality reduction method. And is also 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.
- ↑ J. B. Tenenbaum, V. de Silva, J. C. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science 290, (2000), 2319–2323.
2014
- http://scikit-learn.org/stable/modules/manifold.html#isomap
- QUOTE: One of the earliest approaches to manifold learning is the Isomap algorithm, short for Isometric Mapping. Isomap can be viewed as an extension of Multi-dimensional Scaling (MDS) or Kernel PCA. Isomap seeks a lower-dimensional embedding which maintains geodesic distances between all points. Isomap can be performed with the object
Isomap.
- QUOTE: One of the earliest approaches to manifold learning is the Isomap algorithm, short for Isometric Mapping. Isomap can be viewed as an extension of Multi-dimensional Scaling (MDS) or Kernel PCA. Isomap seeks a lower-dimensional embedding which maintains geodesic distances between all points. Isomap can be performed with the object