An Overview of Principal Component Analysis

Article Properties
Cite
Karamizadeh, Sasan, et al. “An Overview of Principal Component Analysis”. Journal of Signal and Information Processing, vol. 04, no. 03, 2013, pp. 173-5, https://doi.org/10.4236/jsip.2013.43b031.
Karamizadeh, S., Abdullah, S. M., Manaf, A. A., Zamani, M., & Hooman, A. (2013). An Overview of Principal Component Analysis. Journal of Signal and Information Processing, 04(03), 173-175. https://doi.org/10.4236/jsip.2013.43b031
Karamizadeh, Sasan, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, and Alireza Hooman. “An Overview of Principal Component Analysis”. Journal of Signal and Information Processing 04, no. 03 (2013): 173-75. https://doi.org/10.4236/jsip.2013.43b031.
Karamizadeh S, Abdullah SM, Manaf AA, Zamani M, Hooman A. An Overview of Principal Component Analysis. Journal of Signal and Information Processing. 2013;04(03):173-5.
Refrences
Title Journal Journal Categories Citations Publication Date
10.1007/978-3-642-31552-7_43
10.1007/978-3-642-31552-7_43
10.1007/978-3-642-31552-7_43
10.1007/978-3-642-31552-7_43
10.1007/978-3-642-31552-7_43
Citations
Title Journal Journal Categories Citations Publication Date
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  • Science: Science (General): Cybernetics: Information theory
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
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Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation

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Citations Analysis
Category Category Repetition
Science: Biology (General)3
Medicine: Medicine (General): Computer applications to medicine. Medical informatics2
Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry2
Science: Chemistry: Organic chemistry: Biochemistry1
Technology: Mechanical engineering and machinery1
Technology: Manufactures1
Technology: Technology (General): Industrial engineering. Management engineering1
Technology: Engineering (General). Civil engineering (General)1
Science: Science (General): Cybernetics: Information theory1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
Agriculture1
Agriculture: Agriculture (General)1
Agriculture: Plant culture1
Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry: Neurology. Diseases of the nervous system: Psychiatry: Therapeutics. Psychotherapy1
Philosophy. Psychology. Religion: Psychology1
Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry: Neurology. Diseases of the nervous system: Psychiatry1
Social Sciences: Industries. Land use. Labor: Special industries and trades: Energy industries. Energy policy. Fuel trade1
The category Science: Biology (General) 3 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Ranking de desempenho do Governo Federal por funnnes de Governo (Performance Ranking of the Federal Government by Government Functions) and was published in 2014. The most recent citation comes from a 2024 study titled Fault Diagnosis of an Industrial Chemical Process using Machine Learning Algorithms: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). This article reached its peak citation in 2022, with 5 citations. It has been cited in 11 different journals, 18% of which are open access. Among related journals, the Computational Intelligence and Neuroscience cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year