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International Journal of Remote Sensing, 2020
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (feature) reduction strategies through Feature Extraction ...
Md. Palash Uddin +3 more
semanticscholar +1 more source
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (feature) reduction strategies through Feature Extraction ...
Md. Palash Uddin +3 more
semanticscholar +1 more source
Principal components analysis (PCA)
Computers & Geosciences, 1993Principal Components Analysis (PCA) as a method of multivariate statistics was created before the Second World War. However, the wider application of this method only occurred in the 1960s, during the “Quantitative Revolution” in the Natural and Social Sciences.
Andrzej Maćkiewicz, Waldemar Ratajczak
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Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis
IEEE Transactions on Industrial Informatics, 2023To avoid the overfitting phenomenon that appeared in performing graph neural networks (GNNs) on test examples, the feature encoding scheme of GNNs usually introduces the dropout procedure.
Wei Dong +4 more
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PCA-Pruner: Filter pruning by principal component analysis
Journal of Intelligent & Fuzzy Systems, 2022Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their outstanding performance. However, they simultaneously bring enormous computational overhead, making it difficult to deploy to mobile and edge devices. Therefore, researchers use network compression techniques such as quantization, knowledge distillation and ...
Zhang, Wei, Wang, Zhiming
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Image authenticity implementing Principal Component Analysis (PCA)
2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT), 2013The paper addresses the application of finding key features within an image utilizing the process termed the Principal Components Analysis (PCA). Understanding this technique is critical for researchers within biometric fields and the larger cyber security field. Research, found in ASEE 2011 Conference Proceedings, titled “Edge Detectors in Engineering
Suzanna Schmeelk, John Schmeelk
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A Review of Principal Component Analysis Algorithm for Dimensionality Reduction
, 2021Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research.
Basna Mohammed Salih Hasan +1 more
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Principal Component Analysis (PCA) in Ichnology
2021I observed the ichnofabric variable on 600 ichnofabric units in the Samarinda area of Kutai Basin. Five ichnofabric variables are bioturbation index (BI), biodiversity (ID), number of behaviors (NB), penetration depth (PD), and burrow diameter (DM) that perform as a semi-quantitative form. It must process the data with principal component analysis (PCA)
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Identification of the isomers using principal component analysis (PCA) method
AIP Conference Proceedings, 2016In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra.
Kepceoglu, Abdullah +3 more
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