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  2. t-distributed stochastic neighbor embedding - Wikipedia

    en.wikipedia.org/wiki/T-distributed_stochastic...

    t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, [ 1 ] where Laurens van der Maaten and Hinton proposed the t ...

  3. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec can use either of two model architectures to produce these distributed representations of words: continuous bag of words (CBOW) or continuously sliding skip-gram. In both architectures, word2vec considers both individual words and a sliding context window as it iterates over the corpus.

  4. Nonlinear dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_dimensionality...

    Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially existing across non-linear manifolds which cannot be adequately captured by linear decomposition methods, onto lower-dimensional latent manifolds, with the goal of either visualizing ...

  5. Dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Dimensionality_reduction

    T-distributed Stochastic Neighbor Embedding (t-SNE) is a nonlinear dimensionality reduction technique useful for the visualization of high-dimensional datasets. It is not recommended for use in analysis such as clustering or outlier detection since it does not necessarily preserve densities or distances well. [18]

  6. VALCRI - Wikipedia

    en.wikipedia.org/wiki/VALCRI

    VALCRI also employs algorithms such as PCA, MDS, and t-SNE to embed data points into graphical representations. [8] This feature allows for the statistical and mathematical calculation of similarity and correlation levels between different crime data sets through different algorithmic models which each have their own strengths and weaknesses.

  7. Clustering high-dimensional data - Wikipedia

    en.wikipedia.org/wiki/Clustering_high...

    Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...

  8. The 3-Ingredient Appetizer I Always Make for the Holidays

    www.aol.com/3-ingredient-appetizer-always...

    How To Make My 3-Ingredient Smoked Salmon Dip. For 2 1/2 cups, or 6 to 8 servings, you’ll need: 8 ounces cream cheese, room temperature 4 to 6 ounces hot smoked salmon, flaked

  9. Geoffrey Hinton - Wikipedia

    en.wikipedia.org/wiki/Geoffrey_Hinton

    In 2008, he developed the visualization method t-SNE with Laurens van der Maaten. [ 57 ] [ 58 ] In October and November 2017, Hinton published two open access research papers on the theme of capsule neural networks , [ 59 ] [ 60 ] which according to Hinton, are "finally something that works well".