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Apr 9, 2020 · The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph ...
The method is based on a) the correlation matrix eigendecomposition, which effectively produces eigenvectors of the underlying graph and b) efficient procedure ...
This Interest Group is dedicated to the study and development of tools that can produce low-dimensional representations of large and complex data sets.
Oct 13, 2022 · We present this survey to outline the benefits as well as problems associated with the existing graph dimensionality reduction techniques.
Based on the sparse graph, many sparse graph embedding methods have been proposed to extract the low-dimensional features of data. Sparsity Preserving ...
The proposed feature extraction method can learn a set of embedded points in a low-dimensional space by naturally integrating the discriminative information of ...
Jun 30, 2023 · The concept behind dimensionality reduction is that high-dimensional data are dominated by a small number of simple variables.
May 17, 2021 · We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection.
Dimensionality reduction aims at representing high-dimensional data into low-dimensionality space. In order to make sense, the low-dimensional ...
We propose a novel dimensionality reduction method called low-rank Laplacian graph learning (LRLGL) based on a graph embedding learning framework. Our ...
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