Difference between revisions of "Isomap"
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[http://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...] | [http://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...] | ||
[http://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search] | [http://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search] | ||
Revision as of 00:06, 3 February 2019
YouTube search... ...Google search
- AI Solver
- ...find outliers
- Anomaly Detection
- Dimensional Reduction Algorithms
- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
- Local Linear Embedding (LLE)
- Kernel Approximation
- 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.
