Difference between revisions of "Isomap"
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[https://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...] | [https://www.youtube.com/results?search_query=Kernel+Approximation YouTube search...] | ||
[https://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search] | [https://www.google.com/search?q=Kernel+Approximation+machine+learning+ML ...Google search] | ||
| − | * [[AI Solver]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] |
| − | * [[... | + | * [[Embedding]]: [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]] |
| − | |||
* [[Dimensional Reduction]] | * [[Dimensional Reduction]] | ||
| − | * [[Principal Component Analysis (PCA) | + | * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]] |
| − | |||
** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
** [[Local Linear Embedding (LLE)]] | ** [[Local Linear Embedding (LLE)]] | ||
| − | * [[Kernel Trick]] | + | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] |
* [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] | * [https://en.wikipedia.org/wiki/Isomap Isomap | Wikipedia] | ||
* [https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear dimensionality reduction | Wikipedia] | * [https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction Nonlinear dimensionality reduction | Wikipedia] | ||
Revision as of 02:19, 11 July 2023
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
- Dimensional Reduction
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... 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.
