| Project/Area Number |
09480048
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | HOKKAIDO UNIVERSITY |
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Principal Investigator |
MIZUTA Masahiro Hokkaido Univ., Center for Inf. and Multimedia Studies, Prof., 情報メディア教育研究総合センター, 教授 (70174026)
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| Co-Investigator(Kenkyū-buntansha) |
SUZUKAWA Akio Obihiro Univ.Agriculture and Veterinary Medicine, Asso.Prof., 畜産学部, 助教授 (00277287)
MURAI Tetuya Hokkaido Univ., Grad.School of Eng., Asso.Prof., 大学院・工学研究科, 助教授 (90201805)
SATO Yoshiharu Hokkaido Univ., Grad.School of Eng., Prof., 大学院・工学研究科, 教授 (80091461)
IMAI Hideyuki Hokkaido Univ., Grad.School of ENG., Assoc.Prof., 大学院・工学研究科, 助教授 (10213216)
MINAMI Hiroyuki Hokkaido Univ., Center for Inf. and Multimedia Studies, Assoc.Prof., 助教授 (80261395)
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| Project Period (FY) |
1997 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥11,800,000 (Direct Cost: ¥11,800,000)
Fiscal Year 2000: ¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1999: ¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 1998: ¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 1997: ¥5,200,000 (Direct Cost: ¥5,200,000)
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| Keywords | Implicit Function / Multivariate Data Analysis / Data Mining / Latent Structure / Curve Fitting / Sliced Inverse Regression Analysis / Principal Points / Generalized Principal Components Analysis |
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| Research Abstract |
The aim of the research is to construct a series of methods for nonlinear data analysis. Data analysis methods with algebraic curve fitting are fundamental methods in the research. We had studied algebraic curve fitting at first. We developed an algorithm to derive the exact distance between a point and algebraic curve. A method for finding an algebraic curve structures in data is proposed. According to the progress of the research, we also study Generalized Principal Components Analysis and Principal Curves. For related data analysis methods, Sliced Inverse Regression, Functional Data Analysis and Principal Points are studied. Sliced Inverse Regression with Projection Pursuit is proposed and numerical examples show the advantages of the proposed method. A method of nonlinear functional regression analysis is developed.
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