Presentation | 2011-03-28 Regression:Possible : One dimensional elimination by hyperplane fitting Jun FUJIKI, Shotaro AKAHO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, N-1 dimensional hyperplane fitting for N dimensional data is investigated. Firstly, many kinds of regression and/or principal component analysis are systematically classified. Secondly, it is proved that useful property that the global optimum pass through N data points for affine hyperplane regression, and N-1 data points for linear hyperplane regression for some kinds of regression. To evaluate the robustness of the estimated hyperplane by colinearlity of the data, the contribution rate defined in original principal component analysis is extended to many kinds of regressions based on L_2-norm. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | regression / hyperplane fitting / L_p-norm / global optimum / conminatorial optimization |
Paper # | IBISML2010-116 |
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Committee | IBISML |
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Conference Date | 2011/3/21(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) | Regression:Possible : One dimensional elimination by hyperplane fitting |
Sub Title (in English) | |
Keyword(1) | regression |
Keyword(2) | hyperplane fitting |
Keyword(3) | L_p-norm |
Keyword(4) | global optimum |
Keyword(5) | conminatorial optimization |
1st Author's Name | Jun FUJIKI |
1st Author's Affiliation | The National Institute of Advanced Industrial Science and Technology (AIST)() |
2nd Author's Name | Shotaro AKAHO |
2nd Author's Affiliation | The National Institute of Advanced Industrial Science and Technology (AIST) |
Date | 2011-03-28 |
Paper # | IBISML2010-116 |
Volume (vol) | vol.110 |
Number (no) | 476 |
Page | pp.pp.- |
#Pages | 8 |
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