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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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Research on Dimensionality Reduction in Network Traffic Anomaly Detection [PDF]
Jisuanji gongcheng, 2020 To implement anomaly detection for a high dimensional network with mass flow data,data dimensionality should be reduced to relieve transmission and storage burdens from the system.This paper introduces network traffic anomaly detection process and ...
CHEN Liangchen, GAO Shu, LIU Baoxu, TAO Mingfeng
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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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Binary Whale Optimization Algorithm for Dimensionality Reduction
Mathematics, 2020 Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good ...
Abdelazim G. Hussien +4 more
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Cortical spatio-temporal dimensionality reduction for visual grouping [PDF]
, 2014 The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing.
Barbieri, Davide +3 more
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Improving Dimensionality Reduction Projections for Data Visualization
Applied Sciences, 2023 In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
Bardia Rafieian +2 more
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Non-Redundant Spectral Dimensionality Reduction
, 2017 Spectral dimensionality reduction algorithms are widely used in numerous domains, including for recognition, segmentation, tracking and visualization.
A Brun +26 more
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Non-negative Dimensionality Reduction for Mammogram Classification [PDF]
Journal of Electrical and Electronics Engineering, 2009 Directly classifying high dimensional datamay exhibit the ``curse of dimensionality'' issue thatwould negatively influence the classificationperformance with an increase in the computationalload, depending also on the classifier structure.
I. Buciu, A. Gacsadi
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