A Hierarchical Bayesian Approach to Regularization Problems with Multiple Hyperparameters(Special Section on Digital Signal Processing)
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概要
- 論文の詳細を見る
The Tikhonov regularization theory converts ill-posed inverse problems into well-posed problems by putting penalty on the solution sought. Instead of solving an inverse problem, the regularization theory minimizes a weighted sum of ″data error″ and ″penalty″ function, and it has been successfully applied to a variety of problems including tomography, inverse scattering, detection of radiation sources and early vision algorithms. Since the function to be miniized is a weighted sum of functions, one should estimate appropriate weights. This is a problem of Hyperparameter estimation and a vast literature exists. Another problem is how one should compare a particular penalty function (regularizer) with another. This is a special class of model comparison problems which are generally difficult. A Hierachical Bayesian scheme is proposed with multiple hyperparameters in order to cope with data containing subsets which consist of different degree of smoothness. The scheme outperforms the previous scheme with single hyperparameter.
- 社団法人電子情報通信学会の論文
- 2000-08-25
著者
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Takeuchi Ryo
The Department Of Electrical Electronics And Computer Engineering Waseda University
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NAKAZAWA Susumu
the Department of Electrical, Electronics and Computer Engineering, Waseda University
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KOIZUMI Kazuma
the Department of Electrical, Electronics and Computer Engineering, Waseda University
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MATSUMOTO Takashi
the Department of Electrical, Electronics and Computer Engineering, Waseda University
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Koizumi Kazuma
The Department Of Electrical Electronics And Computer Engineering Waseda University
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Nakazawa Susumu
The Department Of Electrical Electronics And Computer Engineering Waseda University
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Matsumoto Takashi
The Department Of Electrical Electronics And Computer Engineering Waseda University
関連論文
- A Hierarchical Bayesian Approach to Regularization Problems with Multiple Hyperparameters(Special Section on Digital Signal Processing)
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