Classification via principal differential analysis

Jang, Eunseong; Lim, Yaeji

doi:10.29220/csam.2021.28.2.135

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…They explored using principal differential analysis to reduce the number of dimensions in functional data sets. Jang and Lim [66] have suggested a classification approach based on principal differential analysis. They combined the principal differential analysis results with logistic regression for binary classification.…”

Section: Development Of the Author Abstract In The Fdamentioning

confidence: 99%

Research Trends on Functional Data Analysis Using Scopus Database: A Bibliometric Analysis

Suhaila,

Hamdan

2023

Mal. J. Fund. Appl. Sci.

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Functional data analysis (FDA) has received significant attention from researchers due to its flexibility and diverse applications in various fields. FDA provides a comprehensive framework for analysing and extracting information from complex and high-dimensional datasets, enabling researchers to obtain insights into the underlying processes, improve modelling, and make accurate predictions. Therefore, understanding the FDA topic and its features and tools, as well as identifying the collaborative networks, are crucial for the development of its research areas. The objective of the present bibliometric study is to analyse the global research trend in FDA areas based on publication outputs, authorships, co-authorships, affiliated countries, and the co-occurrence of author keywords, which will enable researchers to assess the existing knowledge environment, future trends, potential research gaps, and collaboration opportunities. The publications from the year 1989 to 2021 were retrieved from the Scopus database, resulting in 1712 articles in journals after screening. Results have shown that articles published in the Journal of the American Statistical Association received the highest citations. Nearly 43% of the published articles were contributed by the leading authors from the USA, followed by China (11.5%) and Spain (9.4%). According to the QS World University Ranking 2021, eight of the top 20 productive institutions were ranked among the top 100 best universities. The findings indicated that researchers had intensively developed and applied FDA tools and features, such as smoothing, principal component analysis, regression, and clustering, in various domains. In addition, the expansion of FDA tools could be seen based on the recent progress in author keywords. New keywords, including function-on-function regression, function-on-scalar regression, scalar-on-function regression, outlier detection, structural health monitoring, and COVID-19, have arisen recently. Due to public concern about emerging diseases, future FDA work is expected to rise, particularly in the health sciences and biomedical fields.

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Section: Development Of the Author Abstract In The Fdamentioning

confidence: 99%