Results 11 to 20 of about 379,758 (236)
Haisu: Hierarchically supervised nonlinear dimensionality reduction.
PLoS Computational Biology, 2022 We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the ...
Kevin Christopher VanHorn +1 more
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IEEE Access, 2021 Dimensionality reduction is a hot research topic in pattern recognition. Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.
Shuzhi Su, Gang Zhu, Yanmin Zhu
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Frontiers in Ecology and Evolution, 2023 The response surface model has been widely used in slope reliability analysis owing to its efficiency. However, this method still has certain limitations, especially the curse of high dimensionality when considering the spatial variability of ...
Zheng Zhou +12 more
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Dimensionality Reduction Mappings [PDF]
, 2011 A wealth of powerful dimensionality reduction methods has been established which can be used for data visualization and preprocessing. These are accompanied by formal evaluation schemes, which allow a quantitative evaluation along general principles and ...
Biehl, Michael +3 more
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Effective and efficient approach in IoT Botnet detection
Jurnal Ilmiah SINERGI, 2023 Internet of Things (IoT) technology presents an advantage to daily life, but this advantage is not a guarantee of security. This is because cyber-attacks, such as botnets, remain a threat to the user.
Susanto Susanto +4 more
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Dimensionality reduction with image data [PDF]
, 2004 A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a
Benito Bonito, Mónica +1 more
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Dimensionality reduction by LPP‐L21
IET Computer Vision, 2018 Locality preserving projection (LPP) is one of the most representative linear manifold learning methods and well exploits intrinsic structure of data. However, the performance of LPP remarkably degenerate in the presence of outliers.
Shujian Wang +3 more
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mSystems, 2021 Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial ...
George Armstrong +6 more
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Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction [PDF]
, 2010 It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square ...
A D’aspremont +40 more
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Neighbors-Based Graph Construction for Dimensionality Reduction
IEEE Access, 2019 Dimensionality reduction is a fundamental task in the field of data mining and machine learning. In many scenes, examples in high-dimensional space usually lie on low-dimensional manifolds; thus, learning the low-dimensional embedding is important.
Hui Tian +3 more
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