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Complementary dimension reduction
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2020AbstractThe goal of supervised dimension reduction (SDR) is to find a compact yet informative representation of the feature vector. Most SDR algorithms are formulated to solve sequential optimization problems with objective functions being linear functions of the L2 norm of the data, for example, the well‐known Fisher's discriminant analysis (FDA).
Na Cui, Jianjun Hu, Feng Liang 0002
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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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, 2007 When data objects that are the subject of analysis using machine learning techniques are described by a large number of features (i.e. the data is high dimension) it is often beneficial to reduce the dimension of the data.
Pádraig Cunningham
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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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New non-linear dimension reduction methods with application to credit risk ...
Lars Palapies
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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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, 2018 Course module on dimension reduction. This module corresponds to Lectures 9 (Cluster Analysis; November 13) and Lecture 10 (Factor Analysis and Friends; November 20)
Elizabeth Page-Gould
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