Difference between revisions of "T-Distributed Stochastic Neighbor Embedding (t-SNE)"
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* [http://files.knime.com/sites/default/files/inline-images/knime_seventechniquesdatadimreduction.pdf Seven Techniques for Dimensionality Reduction | KNIME] | * [http://files.knime.com/sites/default/files/inline-images/knime_seventechniquesdatadimreduction.pdf Seven Techniques for Dimensionality Reduction | KNIME] | ||
* [[Principal Component Analysis (PCA)]] ...linear | * [[Principal Component Analysis (PCA)]] ...linear | ||
| − | * [http://projector.tensorflow.org/ Embedding Projector] | + | * [[TensorFlow]] |
| + | ** [http://projector.tensorflow.org/ Embedding Projector] | ||
* [[Visualization]] | * [[Visualization]] | ||
* [http://distill.pub/2016/misread-tsne/ How to Use t-SNE Effectively |] [[Creatives#Martin Wattenberg |Martin Wattenberg]] - Distill | * [http://distill.pub/2016/misread-tsne/ How to Use t-SNE Effectively |] [[Creatives#Martin Wattenberg |Martin Wattenberg]] - Distill | ||
Revision as of 15:15, 22 August 2020
Youtube search... ...Google search
- Principal Component Analysis (PCA)
- Embedding
- Dimensional Reduction Algorithms
- Softmax
- Pooling / Sub-sampling: Max, Mean
- (Deep) Convolutional Neural Network (DCNN/CNN)
- Seven Techniques for Dimensionality Reduction | KNIME
- Principal Component Analysis (PCA) ...linear
- TensorFlow
- Visualization
- How to Use t-SNE Effectively | Martin Wattenberg - Distill
a machine learning algorithm for visualization developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability. Wikipedia
