Dimensionality reduction of quality of life indicators
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2012 Selecting indicators for assessing the quality of life at the regional level is not unambigous. Currently, there are no precisely defined indicators that would give comprehensive information about the quality of life on a local level.
Andrea Jindrová, Julie Poláčková
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Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing [PDF]
arXiv, 2018 Dimensionality reduction is an integral part of data visualization. It is a process that obtains a structure preserving low-dimensional representation of the high-dimensional data. Two common criteria can be used to achieve a dimensionality reduction: distance preservation and topology preservation. Inspired by recent work in topological data analysis,
arxiv
On the existence of infinite-dimensional generalized harish-chandra modules [PDF]
arXiv, 2018 We prove a general existence result for infinite-dimensional admissible (g;k)-modules, where g is a reductive finite-dimensional complex Lie algebra and k is a reductive in g algebraic subalgebra.
arxiv
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold [PDF]
arXiv, 2019 Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals. This paper presents a proposal of a novel Riemannian joint dimensionality reduction and dictionary learning (R-JDRDL) on symmetric positive definite (SPD) manifolds for classification tasks.
arxiv
Criteria for good reduction of proper polycurves [PDF]
arXiv, 2018 We give good reduction criteria for hyperbolic polycurves, i.e., successive extensions of families of curves, under mild assumption. These criteria are higher dimensional version of the good reduction criterion for hyperbolic curves given by Oda and Tamagawa.
arxiv
A remark about dimension reduction for supremal functionals: the case with convex domains [PDF]
arXiv, 2012 An application of dimensional reduction results for gradient constrained problems is provided for 3D-2D dimension reduction for supremal functionals, in the case when the domain is convex.
arxiv
Multiple Kernel Spectral Regression for Dimensionality Reduction
Journal of Applied Mathematics, 2013 Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples.
Bing Liu, Shixiong Xia, Yong Zhou
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Cascade Support Vector Machines with Dimensionality Reduction
Applied Computational Intelligence and Soft Computing, 2015 Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
Oliver Kramer
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BIP: A dimensionality reduction for image indexing
ICT Express, 2019 Searching on internet is one of the daily task done by millions of users around the Globe. There is an urge for effective indexing scheme for unstructured data, which provide better search results. The image, content report, and site pages are said to be
Minu R.I. +3 more
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Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification [PDF]
arXivWe introduce a method to construct a stochastic surrogate model from the results of dimensionality reduction in forward uncertainty quantification. The hypothesis is that the high-dimensional input augmented by the output of a computational model admits a low-dimensional representation.
arxiv