Results 211 to 220 of about 294,873 (258)
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Communications of the ACM, 2010
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). Text documents may be represented in a vector space where each word in the dictionary incurs a dimension.
Nir Ailon, Bernard Chazelle
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Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). Text documents may be represented in a vector space where each word in the dictionary incurs a dimension.
Nir Ailon, Bernard Chazelle
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DIMENSION REDUCTION FOR DISCRETE SYSTEMS
Applied and Industrial Mathematics in Italy II, 2007Object of this talk is the description of the overall behaviour of variational pair-interaction lattice systems defined on `thin' domains of ; i.e. on domains consisting on a finite number of mutually interacting copies of a portion of a -dimensional discrete lattice.
ALICANDRO, Roberto +2 more
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The Journal of Experimental Education, 1972
ABSTRACTIn multivariate analysis of variance the canonical variates of one effect may be correlated with the canonical variates of another effect. When the two effects are an interaction and a main effect this correlation interferes with the interpretation of the main effect.
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ABSTRACTIn multivariate analysis of variance the canonical variates of one effect may be correlated with the canonical variates of another effect. When the two effects are an interaction and a main effect this correlation interferes with the interpretation of the main effect.
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Fast nonlinear dimension reduction
IEEE International Conference on Neural Networks, 2002A new algorithm for nonlinear dimension reduction is presented. The algorithm builds a piecewise linear model of the data. It provides compression that is superior to the globally linear model produced by principal component analysis. On several examples the piecewise linear model also provides compression that is superior to the global nonlinear model
Nandakishore Kambhatla, Todd K. Leen
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PLS and dimension reduction for classification
Computational Statistics, 2007Different techniques of dimension reduction in classification problems are compared. The main attention is paid to the partial least squares (PLS) and its modification, oriented PLS (OPLS). These techniques are compared to the principal components analysis (PCA) used as pre-processing before linear discriminant analysis (LDA). A ridge-like technique is
Yushu Liu, William S. Rayens
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Groupwise Bayesian dimension reduction
2017 International Joint Conference on Neural Networks (IJCNN), 2017Nearly all existing estimations of the central subspace in regression take the frequentist approach. However, when the predictors fall naturally into a number of groups, these frequentist methods treat all predictors indiscriminately and can result in loss of the group-specific relation between the response and the predictors.
Bo Zhang +3 more
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Reduction of Dimension and Turnpike Theory
IFAC Proceedings Volumes, 1986Abstract We give here the description of asymptotical properties of solutions to a class of scale-invariant dynamic optimization models arising in mathematical economics. These results are based on the geometric theory of Hamiltonian differential equations possessing the group of symmetries.
A.D. Tsvirkun, S.Yu. Yakovenko
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Canonical kernel dimension reduction
Computational Statistics & Data Analysis, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chenyang Tao, Jianfeng Feng
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Sufficient Dimension Reduction and Kernel Dimension Reduction
2023Benyamin Ghojogh +3 more
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A Brief Survey of Dimension Reduction
2018Dimension reduction problem is a big concern which can reduce the scale of a database and keep the main features of these data simultaneously. This paper aims at reviewing and comparing different dimension reduction algorithms. Mainly, the performances of four basic algorithms (PCA, LDA, LLE and LE), their improved methods and deep learning methods are
Li Song +4 more
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