Faculty Profiles - IIZUKA Masaya

MISC - IIZUKA Masaya

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非計量主成分分析の加速化 : 実データへの適用(一般セッション数学・統計(4))

黒田正博, 榊原道夫, 飯塚誠也, 森裕一

日本行動計量学会大会発表論文抄録集 40 399 - 400 2012.9

電子教科書における動的教材

張梁, 飯塚誠也, 垂水共之

日本計算機統計学会シンポジウム論文集 26th 2012

電子教科書の可能性について

垂水共之, 張梁, 飯塚誠也

統計関連学会連合大会講演報告集 2012 2012

EPUB3.0による統計教科書

張梁, 飯塚誠也, 垂水共之

日本計算機統計学会大会論文集 26th 2012

Acceleration of the alternating least squares algorithm for principal components analysis

Masahiro Kuroda, Yuichi Mori, Masaya Iizuka, Michio Sakakihara

Computational Statistics and Data Analysis 55 ( 1 ) 143 - 153 2011.1

Language：English Publisher：ELSEVIER SCIENCE BV

Principal components analysis (PCA) is a popular descriptive multivariate method for handling quantitative data and it can be extended to deal with qualitative data and mixed measurement level data. The existing algorithms for extended PCA are PRINCIPALS of Young et al. (1978) and PRINCALS of Gifi (1989) in which the alternating least squares algorithm is utilized. These algorithms based on the least squares estimation may require many iterations in their application to very large data sets and variable selection problems and may take a long time to converge. In this paper, we derive a new iterative algorithm for accelerating the convergence of PRINCIPALS and PRINCALS by using the vector ε algorithm of Wynn (1962). The proposed acceleration algorithm speeds up the convergence of the sequence of the parameter estimates obtained from PRINCIPALS or PRINCALS. Numerical experiments illustrate the potential of the proposed acceleration algorithm. © 2010 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.csda.2010.06.001

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Variable selection in principal components analysis of qualitative data using the accelerated ALS algorithm.

Kuroda, M, Mori, Y, Iizuka, M, Sakakihara, M

Proceedings of the 7th conference of Asian Regional Section of IASC 83 - 83 2011

質的データの主成分分析と計算の加速化

森裕一, 飯塚誠也, 黒田正博, 榊原道夫

日本計算機統計学会大会論文集 25th 2011

交互最小二乗法におけるvector ε加速化法の改良

黒田正博, 森裕一, 飯塚誠也, 榊原道夫

日本計算機統計学会大会論文集 24th 2010

交互最小二乗法の加速化-数値的検討

黒田正博, 森裕一, 飯塚誠也, 榊原道夫

統計関連学会連合大会講演報告集 2010 2010

Variable selection in multivariate methods using global score estimation

Kaoru Fueda, Masaya Iizuka, Yuichi Mori

Computational Statistics 24 ( 1 ) 127 - 144 2009.2

Language：English Publisher：SPRINGER HEIDELBERG

A variable selection method using global score estimation is proposed, which is applicable as a selection criterion in any multivariate method without external variables such as principal component analysis, factor analysis and correspondence analysis. This method selects a subset of variables by which we approximate the original global scores as much as possible in the context of least squares, where the global scores, e.g. principal component scores, factor scores and individual scores, are computed based on the selected variables. Global scores are usually orthogonal. Therefore, the estimated global scores should be restricted to being mutually orthogonal. According to how to satisfy that restriction, we propose three computational steps to estimate the scores. Example data is analyzed to demonstrate the performance and usefulness of the proposed method, in which the proposed algorithm is evaluated and the results obtained using four cost-saving selection procedures are compared. This example shows that combining these steps and procedures yields more accurate results quickly. © 2008 Springer-Verlag.

DOI： 10.1007/s00180-008-0109-9

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主成分分析における変数選択のExcelアプリケーション

張梁, 飯塚誠也, 森裕一

日本計算機統計学会シンポジウム論文集 23rd 2009