AI Technology Analysis using Partial Least Square Regression (부분최소제곱 회귀분석을 이용한 인공지능 기술분석)

Choi.Jun-Hyeog (최준혁); Sung-Hae Jun (전성해)

doi:10.9708/jksci.2020.25.03.109

AI Technology Analysis using Partial Least Square Regression

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(3), pp.109-115
DOI : 10.9708/jksci.2020.25.03.109
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : November 29, 2019
Accepted : February 25, 2020
Published : March 31, 2020

Choi.Jun-Hyeog ¹, Sung-Hae Jun ²

¹김포대학교
²청주대학교

Accredited

ABSTRACT

In this paper, we propose an artificial intelligence(AI) technology analysis using partial least square(PLS) regression model. AI technology is now affecting most areas of our society. So, it is necessary to understand this technology. To analyze the AI technology, we collect the patent documents related to AI from the patent databases in the world. We extract AI technology keywords from the patent documents by text mining techniques. In addition, we analyze the AI keyword data by PLS regression model. This regression model is based on the technique of partial least squares used in the advanced analyses such as bioinformatics, social science, and engineering. To show the performance of our proposed method, we make experiments using AI patent documents, and we illustrate how our research can be applied to real problems. This paper is applicable not only to AI technology but also to other technological fields. This also contributes to understanding other various technologies by PLS regression analysis.

KEYWORDS

Artificial Intelligence, Partial Least Squares, Regression Analysis, Patent Data, Technology Analysis

Citation status

* References for papers published after 2023 are currently being built.

[book] A. T. Roper / 2011 / Forecasting and Management of Technology / John Wiley & Sons

[journal] 이소영 / 2019 / AI Chatbot Providing Real-Time Public Transportation and Route Information / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 24(7) : 9~17

[journal] 정주호 / 2019 / Intelligent User Pattern Recognition based on Vision, Audio and Activity for Abnormal Event Detections of Single Households / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 24(5) : 59~66

[journal] 최승윤 / 2018 / Controller Learning Method of Self-driving Bicycle Using State-of-the-art Deep Reinforcement Learning Algorithms / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 23(10) : 23~31

[journal] 최종인 / 2019 / Technology Trends for Motion Synthesis and Control of 3D Character / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 24(4) : 19~26

[book] S. Russell / 2014 / Artificial Intelligence: A Modern Approach, Third Edition / Pearson

[book] K. P. Murphy / 2012 / Machine Learning: a probabilistic perspective / MIT Press

[book] S. Theodoridis / 2015 / Machine Learning A Bayesian and Optimization Perspective / Elsevier

[journal] M. G. Gustafsson / 2001 / A Probabilistic Derivation of the Partial Least-Squares Algorithm / Journal of Chemical Information and Computer Sciences 41(2) : 288~294

[confproc] L. Sun / 2009 / On the Equivalence Between Canonical Correlation Analysis and Orthonormalized Partial Least Squares / Proceedings of the 21st international joint conference on Artifical intelligence : 1230~1235

[journal] M. Barker / 2003 / Partial least squares for discrimination / Journal of Chemometrics 17(3) : 166~173

[book] S. M. Ross / 2017 / Introductory Statistics, Fourth Edition / Academic Press Elsevier

[journal] J. Keller / 2014 / The influence of information and communication technology(ICT)on future foresight processes–Results from a Delphi survey / Technological Forecasting and Social Change 85 : 81~92

[journal] H. A. Linstone / 2011 / Delphi : A Brief Look Backward and Forward / Technological Forecasting and Social Change 78(9) : 1712~1719

[journal] S. Jun / 2015 / A novel method of IP R&D using patent analysis and expert survey / Queen Mary Journal of Intellectual Property 5(4) : 474~494

[journal] S. Park / 2017 / Technology Analysis of Global Smart Light Emitting Diode(LED)Development Using Patent Data / Sustainability 9(8) : 1363~

[journal] J. Kim / 2019 / Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models / Sustainability 11(13) : 3597~

[journal] J. Kim / 2019 / Patent data analysis using functional count data model / Soft Computing 23(18) : 8815~8826

[journal] S. Jun / 2018 / Bayesian Count Data Modeling for Finding Technological Sustainability / Sustainability 10(9) : 3220~

[confproc] R. Rosipal / 2005 / Overview and recent advances in partial least squares / Proceedings of International Statistical and Optimization Perspectives Workshop : 34~51

[web] USPTO / 2019 / The United States Patent and Trademark Office / http://www.uspto.gov

[web] WIPSON / 2019 / WIPS Corporation / http://www.wipson.com,http://global.wipscorp.com

[web] R Development Core Team / 2019 / R: A language and environment for statistical computing / R Foundation for Statistical Computing / http://www.R-project.org

[journal] I. Feinerer / 2008 / Text mining infrastructure in R / Journal of Statistical Software 25(5) : 1~54

[other] I. Feinerer / 2019 / Package ‘tm’ Ver. 0.7-6, Text Mining Package / CRAN of R project

This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

AI Technology Analysis using Partial Least Square Regression

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

AI Technology Analysis using Partial Least Square Regression

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (1)

REFERENCES (25) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.