Jan 8, 2020 · In this paper, we propose a new dimensionality reduction method for the unbalanced semi-supervised problem, called sparse locality preserving projection (SLPP ...
Here, we preserve the geometric structure of the rest unlabeled samples and their k-nearest neighbors after increasing the number of labeled samples by label ...
Here, we preserve the geometric structure of the rest unlabeled samples and their k-nearest neighbors after increasing the number of labeled samples by label ...
Semi-supervised locality preserving projection (SSLPP) [19] improves the effect of DR by keeping the structural relationship of labeled samples and preserving ...
For high-dimensional data, we can give a low-dimensional embedding result for both discriminating multi-class sub-manifolds and preserving local manifold ...
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The effect of different p on UCI data sets - ResearchGate
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We propose a new dimensionality reduction method for the unbalanced semi-supervised problem, which is called sparse locality preserving projection (SLPP) [47] .
May 20, 2020 · Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation.
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What is supervised dimensionality reduction?
In this letter, a semi-supervised dimensionality reduction method named Discriminative Sparsity Preserving Projection (DSPP) is proposed which obtains an ...
Feb 1, 2024 · ... Semi-supervised dimensionality reduction via sparse locality preserving projection, Appl. Intell. 50 (4) (2020) 1222–1232. Google Scholar.
In this paper, we extend PCPM to semi-supervised setting. The labeled data points are used to maximize the separability between different classes and the ...