Presentation | 2012-11-08 An Efficient Input Variable Selection for a Linear Regression Model by NC Spectral Clustering Koichi FUJIWARA, Hiroshi SAWADA, Manabu KANO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Linear regression models have been widely accepted in many scientific and engineering fields for the estimation or interpretation of phenomena. When a linear regression model is built, appropriate input variables have to be selected to achieve high estimation performance. This work proposes new methodologies for selecting input variables for linear regression models using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In the present work, NCSC is used for variable group construction, and a few variable groups are selected by their contribution to estimates; it is referred to as NCSC-based variable selection (NCSC-VS). The usefulness of the proposed NCSC-VS is demonstrated through an industrial application to a chemical process. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Linear regression / Variable selection / Spectral clustering / Partial least squares |
Paper # | IBISML2012-84 |
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Committee | IBISML |
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Conference Date | 2012/10/31(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | An Efficient Input Variable Selection for a Linear Regression Model by NC Spectral Clustering |
Sub Title (in English) | |
Keyword(1) | Linear regression |
Keyword(2) | Variable selection |
Keyword(3) | Spectral clustering |
Keyword(4) | Partial least squares |
1st Author's Name | Koichi FUJIWARA |
1st Author's Affiliation | Kyoto University() |
2nd Author's Name | Hiroshi SAWADA |
2nd Author's Affiliation | NTT NTT Communication Science Laboratories |
3rd Author's Name | Manabu KANO |
3rd Author's Affiliation | Kyoto University |
Date | 2012-11-08 |
Paper # | IBISML2012-84 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 8 |
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