linear embedding - traducción al ruso
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linear embedding - traducción al ruso

SUMMARY OF ALGORITHMS FOR NONLINEAR DIMENSIONALITY REDUCTION
Locally Linear Embedding; Non-linear dimensionality reduction; Manifold learning; Locally linear embedding; Locally linear embeddings; Local linear embedding; Uniform manifold approximation and projection; Uniform Manifold Approximation and Projection; Probabilistic learning on manifolds
  • PCA (a linear dimensionality reduction algorithm) is used to reduce this same dataset into two dimensions, the resulting values are not so well organized.
  • Hessian LLE]] algorithms as implemented by the Modular Data Processing toolkit.
  •  Plot of the two-dimensional points that results from using a NLDR algorithm. In this case, Manifold Sculpting is used to reduce the data into just two dimensions (rotation and scale).
  • principal component]] is presented by a blue straight line. Data points are the small grey circles. For PCA, the [[Fraction of variance unexplained]] in this example is 23.23%, for SOM it is 6.86%.<ref>The illustration is prepared using free software: E.M. Mirkes, [http://www.math.le.ac.uk/people/ag153/homepage/PCA_SOM/PCA_SOM.html Principal Component Analysis and Self-Organizing Maps: applet]. University of Leicester, 2011</ref>
  • Institut Curie]], Paris.</ref><ref>A. Zinovyev, [https://www.ihes.fr/~zinovyev/vida/ViDaExpert/ViDaOverView.pdf ViDaExpert overview], IHES ([[Institut des Hautes Études Scientifiques]]), Bures-Sur-Yvette, Île-de-France.</ref>

linear embedding         
  • A [[Farey diagram]]
TYPE OF GRAPH DRAWING
Linear embedding

математика

линейное вложение

embedding         
INJECTIVE AND STRUCTURE-PRESERVING MAP
Embedding (topology); Topological embedding; Isometric embedding; Isometric immersion; Abstract embedding; Isometric imbedding; Embedding (field theory); Metric embedding; Local embedding; Embedding (mathematics); Locally injective function

общая лексика

заделка

заливка (препарата)

имплантация оплодотворённой яйцеклетки

встраивание [объектов]

вложение

вставка

внедрение

заделывание

вдавливание

утапливание

гистология

заключение

linear transformation         
  • The function f:\R^2 \to \R^2 with f(x, y) = (2x, y) is a linear map. This function scales the x component of a vector by the factor 2.
  • The function f(x, y) = (2x, y) is additive: It doesn't matter whether vectors are first added and then mapped or whether they are mapped and finally added: f(\mathbf a + \mathbf b) = f(\mathbf a) + f(\mathbf b)
  • The function f(x, y) = (2x, y) is homogeneous: It doesn't matter whether a vector is first scaled and then mapped or first mapped and then scaled: f(\lambda \mathbf a) = \lambda f(\mathbf a)
MAPPING THAT PRESERVES THE OPERATIONS OF ADDITION AND SCALAR MULTIPLICATION
Linear operator; Linear mapping; Linear transformations; Linear operators; Linear transform; Linear maps; Linear isomorphism; Linear isomorphic; Linear Transformation; Linear Transformations; Linear Operator; Homogeneous linear transformation; User:The Uber Ninja/X3; Linear transformation; Bijective linear map; Nonlinear operator; Linear Schrödinger Operator; Vector space homomorphism; Vector space isomorphism; Linear extension of a function; Linear extension (linear algebra); Extend by linearity; Linear endomorphism

['liniətrænsfə'meiʃ(ə)n]

общая лексика

линейное преобразование

Definición

linear map
<mathematics> (Or "linear transformation") A function from a vector space to a vector space which respects the additive and multiplicative structures of the two: that is, for any two vectors, u, v, in the source vector space and any scalar, k, in the field over which it is a vector space, a linear map f satisfies f(u+kv) = f(u) + kf(v). (1996-09-30)

Wikipedia

Nonlinear dimensionality reduction

Nonlinear dimensionality reduction, also known as manifold learning, refers to various related techniques that aim to project high-dimensional data onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.

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