Results 41 to 50 of about 8,013,557 (321)
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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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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International Conference on Image and Signal Processing, 2020 Dimensionality reduction is widely used in machine learning and big data analytics since it helps to analyze and to visualize large, high-dimensional datasets.
M. Allaoui, M. L. Kherfi, A. Cheriet
semanticscholar +1 more source
The Effect of Different Dimensionality Reduction Techniques on Machine Learning Overfitting Problem
, 2021 In most conditions, it is a problematic mission for a machine-learning model with a data record, which has various attributes, to be trained. There is always a proportional relationship between the increase of model features and the arrival to the ...
M. A. Salam +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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Journal of Big Data, 2020 Telecom Companies logs customer’s actions which generate a huge amount of data that can bring important findings related to customer’s behavior and needs.
Maha Alkhayrat +2 more
semanticscholar +1 more source
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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Regional-scale groundwater analysis with dimensionality reduction [PDF]
Natural Hazards and Earth System SciencesGiven the importance of groundwater for freshwater provision and groundwater-dependent ecosystems, understanding climate effects on groundwater changes at a regional scale is essential.
M. Somogyvári +9 more
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