Results 11 to 20 of about 1,072,724 (364)
Validation of Nonlinear PCA [PDF]
Neural Processing Letters, 2012 Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied ...
A Herman +26 more
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Canadian Journal of Statistics, 2023 AbstractMahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing a rerandomization strategy. However, this criterion may not work well for high‐dimensional cases because it balances all orthogonalized covariates equally.
Hengtao Zhang +2 more
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Hybrid modeling and prediction of oyster norovirus outbreaks
Journal of Water and Health, 2021 This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite ...
Shima Shamkhali Chenar, Zhiqiang Deng
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Principal component analysis of a canning determinate tomato collection in the IPGR, Sadovo - Bulgaria [PDF]
Agroznanje, 2012 The success of a tomato breeding programme largely depends on the study of initial material and symptoms studied as well as manifestations of dependence between them. The study was conducted during the period 2008-2011 in the IPGR, Bulgaria.
Krasteva Liliya +2 more
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Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations
Geosfera Indonesia, 2021 Landslide is a natural disaster that occurs mostly in hill areas. Landslide hazard mapping is used to classify the prone areas to mitigate the risk of landslide hazards.
Ilyas Ahmad Huqqani +2 more
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KPCA over PCA to assess urban resilience to floods [PDF]
E3S Web of Conferences, 2021 Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to ...
Satour Narjiss +3 more
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Upregulated wnt-11 and mir-21 expression trigger epithelial mesenchymal transition in aggressive prostate cancer cells [PDF]
, 2020 Prostate cancer (PCa) is the second-leading cause of cancer-related death among men. microRNAs have been identified as having potential roles in tumorigenesis.
Arisan, E.D. +15 more
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SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2018 As an unsupervised dimensionality reduction method, the principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image (HSI) processing and analysis tasks.
Junjun Jiang +5 more
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Geosfera Indonesia, 2021 The use of intensive chemical inputs causes lower availability of nutrients, organic matter, cation exchange capacity, and soil degradation.Therefore, this study aims to assess the soil quality index (SQI) for paddy fields in Jember, East Java, Indonesia.
Putri Tunjung Sari +3 more
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A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU
Electronics, 2020 The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of ...
S. Bhattacharya +7 more
semanticscholar +1 more source