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product dimensionality

См. также в других словарях:

  • Nonlinear dimensionality reduction — High dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lies on an embedded non linear manifold within… …   Wikipedia

  • inner product — noun A generalization of the dot product for vectors of any dimensionality that may or may not be complex numbered …   Wiktionary

  • Principal component analysis — PCA of a multivariate Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the (0.878, 0.478) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by… …   Wikipedia

  • Quantum decoherence — Quantum mechanics Uncertainty principle …   Wikipedia

  • Density of states — Condensed matter physics Phases · Phase tr …   Wikipedia

  • Metal-organic framework — Metal Organic Frameworks are crystalline compounds consisting of metal ions or clusters coordinated to often rigid organic molecules to form one , two , or three dimensional structures that can be porous. In some cases, the pores are stable to… …   Wikipedia

  • Dimension reduction — For dimensional reduction in physics, see Dimensional reduction. In machine learning, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature… …   Wikipedia

  • Singular value decomposition — Visualization of the SVD of a 2 dimensional, real shearing matrix M. First, we see the unit disc in blue together with the two canonical unit vectors. We then see the action of M, which distorts the disk to an ellipse. The SVD decomposes M into… …   Wikipedia

  • Principal components analysis — Principal component analysis (PCA) is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the discrete Karhunen Loève transform (KLT),… …   Wikipedia

  • Semidefinite embedding — (SDE) or maximum variance unfolding (MVU) is an algorithm in computer science, that uses semidefinite programming to perform non linear dimensionality reduction of high dimensional vectorial input data. Non linear dimensionality reduction… …   Wikipedia

  • Multidimensional scaling — (MDS) is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item–item similarities,… …   Wikipedia

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