Difference between revisions of "T-Distributed Stochastic Neighbor Embedding (t-SNE)"

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(Created page with "[http://www.youtube.com/results?search_query=T-SNE+Dimensional+Reduction+Algorithm Youtube search...] * Principle Component Analysis (PCA) * Softmax * Pooling / Sub...")
 
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[http://www.youtube.com/results?search_query=T-SNE+Dimensional+Reduction+Algorithm Youtube search...]
 
[http://www.youtube.com/results?search_query=T-SNE+Dimensional+Reduction+Algorithm Youtube search...]
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[http://www.google.com/search?q=T-SNE+Dimensional+Reduction+Algorithm ...Google search]
  
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* [[Principle Component Analysis (PCA)]]
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* [[Principal Component Analysis (PCA)]]
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* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
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** [[Local Linear Embedding (LLE)]]
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* [[Dimensional Reduction]] Algorithms
 
* [[Softmax]]
 
* [[Softmax]]
 
* [[Pooling / Sub-sampling: Max, Mean]]
 
* [[Pooling / Sub-sampling: Max, Mean]]
 
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 
* [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]
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* [[Principal Component Analysis (PCA)]] ...linear
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* [[TensorFlow]]
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** [http://projector.tensorflow.org/ Embedding Projector]
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* [[Visualization]]
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* [http://distill.pub/2016/misread-tsne/ How to Use t-SNE Effectively |]  [[Creatives#Martin Wattenberg |Martin Wattenberg]] - Distill
  
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a machine learning algorithm for visualization developed by Laurens van der Maaten and [[Creatives#Geoffrey Hinton |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. [http://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding Wikipedia]
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http://miro.medium.com/max/1396/1*RmG8qwjGGbp_CAXA8eDISQ.png

Latest revision as of 09:48, 13 September 2023

Youtube search... ...Google search

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



1*RmG8qwjGGbp_CAXA8eDISQ.png

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