Analysis of a complex of statistical variables into principal components.

Principal component analysis

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Principal component analysis: A review and recent developments

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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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Machine learning: Algorithms, real-world applications and research directions

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Generative Adversarial Networks: An Overview

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Approximate nearest neighbors: towards removing the curse of dimensionality

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Probabilistic Principal Component Analysis

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Principal component analysis

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Relation of sample size to the stability of component patterns.

Guadagnoli, E., & Velicer, W. F. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.2.265 · Cited by 2,880

Transformational leadership, transactional leadership, locus of control, and support for innovation: Key predictors of consolidated-business-unit performance.

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Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

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Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

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Machine Learning Approaches for Clinical Psychology and Psychiatry

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966

Machine learning for internet of things data analysis: a survey

Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921

Self-report captures 27 distinct categories of emotion bridged by continuous gradients

Cowen, A. S., & Keltner, D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1702247114 · Cited by 897

Historical review of OCR research and development

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Designing Theory-Driven User-Centric Explainable AI

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Understanding evaluation of faces on social dimensions

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Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level

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Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471

Variational Quantum Computation of Excited States

Higgott, O., Wang, D., & Brierley, S. (2019) · Quantum · doi:10.22331/q-2019-07-01-156 · Cited by 463

A comprehensive review on deep learning-based methods for video anomaly detection

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Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., Allen, M., Reddy, J. S., Hoffman, G. E., Dang, K. K., Calley, J., Ebert, P. J., Eddy, J., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00642-8 · Cited by 318

Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

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Gaining insights from social media language: Methodologies and challenges.

Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016) · Psychological Methods · doi:10.1037/met0000091 · Cited by 221

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

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A comparison of the effects of three GM corn varieties on mammalian health

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Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework

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Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution

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On the relationship between sloppiness and identifiability

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Statistical analysis of proteomics data: A review on feature selection

Lualdi, M., & Fasano, M. (2019) · Journal of Proteomics · doi:10.1016/j.jprot.2018.12.004 · Cited by 93

Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls

Behravan, H., Hartikainen, J. M., Tengström, M., Pylkäs, K., Winqvist, R., Kosma, V., & Mannermaa, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-31573-5 · Cited by 75

Assessment of the effect of high or low protein diet on the human urine metabolome as measured by NMR

Rasmussen, L. G., Winning, H., Savorani, F., Toft, H., Larsen, T. M., Dragsted, L. O., Astrup, A., & Engelsen, S. B. (2012) · Nutrients · doi:10.3390/nu4020112 · Cited by 81

Chemometric analysis of NMR and GC datasets for chemotype characterization of essential oils from different species of Ocimum.

Freitas, J. V. B., Alves Filho, E. G., Silva, L. M. A., Zocolo, G. J., de Brito, E. S., & Gramosa, N. V. (2018) · Talanta · doi:10.1016/j.talanta.2017.12.053 · Cited by 81

NMR spectroscopy and chemometrics to evaluate different processing of coconut water

Sucupira, N. R., Alves Filho, E. G., Silva, L. M. A., de Brito, E. S., Wurlitzer, N. J., & Sousa, P. H. M. (2017) · Food Chemistry · doi:10.1016/j.foodchem.2016.08.035 · Cited by 68

A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia

Garali, I., Adanyeguh, I. M., Ichou, F., Perlbarg, V., Seyer, A., Colsch, B., Moszer, I., Guillemot, V., Durr, A., Mochel, F., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx060 · Cited by 68

Machine learning action parameters in lattice quantum chromodynamics

Shanahan, P. E., Trewartha, D., & Detmold, W. (2018) · Physical Review D · doi:10.1103/physrevd.97.094506 · Cited by 65

Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning

Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63

How subchronic and chronic health effects can be neglected for GMOs, pesticides or chemicals

Séralini, G.-E., de Vendômois, J. S., Cellier, D., Sultan, C., Buiatti, M., Gallagher, L., Antoniou, M., & Dronamraju, K. R. (2009) · International Journal of Biological Sciences · doi:10.7150/ijbs.5.438 · Cited by 57

Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis

Boye, A. T., Kristiansen, U. Q., Billinger, M., Nascimento, O. F. D., & Farina, D. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2008.05.001 · Cited by 45

Phenotyping chronic tinnitus patients using self-report questionnaire data: cluster analysis and visual comparison

Niemann, U., Brueggemann, P., Boecking, B., Mebus, W., Rose, M., Spiliopoulou, M., & Mazurek, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-73402-8 · Cited by 41

Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems

Herring, C. A., Chen, B., McKinley, E. T., & Lau, K. S. (2018) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2018.01.023 · Cited by 35

Sniffing out the hypoxia volatile metabolic signature of Aspergillus fumigatus

Rees, C. A., Stefanuto, P.-H., Beattie, S. R., Bultman, K. M., Cramer, R. A., & Hill, J. E. (2017) · Journal of Breath Research · doi:10.1088/1752-7163/aa7b3e · Cited by 29

Spacecraft Pose Estimation using Principal Component Analysis and a Monocular Camera

Shi, J.-F., Ulrich, S., & Ruel, S. (2017) · AIAA Guidance, Navigation, and Control Conference · doi:10.2514/6.2017-1034 · Cited by 25

Transitions from mono- to co- to tri-culture uniquely affect gene expression in breast cancer, stromal, and immune compartments

Regier, M. C., Maccoux, L. J., Weinberger, E. M., Regehr, K. J., Berry, S. M., Beebe, D. J., & Alarid, E. T. (2016) · Biomedical Microdevices · doi:10.1007/s10544-016-0083-x · Cited by 23

Climate change as a veiled driver of migration in Bangladesh and Ghana

Fernández, S., Arce, G., García-Alaminos, Á., Cazcarro, I., & Arto, I. (2024) · Science of The Total Environment · doi:10.1016/j.scitotenv.2024.171210 · Cited by 22

Multivariate data analysis and metabolic profiling of artemisinin and related compounds in high yielding varieties of Artemisia annua field-grown in Madagascar

Suberu, J., Gromski, P. S., Nordon, A., & Lapkin, A. (2016) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2015.10.003 · Cited by 19

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Postpartum Changes in Levator Plate Shape and Genital Hiatus Size After Vaginal Delivery

Schmidt, P., Swenson, C. W., DeLancey, J. O., & Chen, L. (2024) · International Urogynecology Journal · doi:10.1007/s00192-024-05813-4 · Cited by 3

Turbofan Engine RUL Prediction using ICA and Machine Learning Algorithms

Aribi, Y., Boutora, S., Boushaki Zamoum, P. R., Menasria, H., Abdellaoui, A., & Kouzou, P. A. (2023) · 2023 20th International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd58187.2023.10411295 · Cited by 2