Entropic risk minimization for nonparametric estimation of mixing distributions
We discuss a nonparametric estimation method for the mixing distributions in mixture models. The problem is formalized as a minimization of a one-parameter objective functional, which becomes the maximum likelihood estimation or the kernel vector quantization in special cases. Generalizing the theor...
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| Veröffentlicht in: | Machine learning 2015-04, Vol.99 (1), p.119-136 |
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| container_end_page | 136 |
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| container_title | Machine learning |
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| creator | Watanabe, Kazuho Ikeda, Shiro |
| description | We discuss a nonparametric estimation method for the mixing distributions in mixture models. The problem is formalized as a minimization of a one-parameter objective functional, which becomes the maximum likelihood estimation or the kernel vector quantization in special cases. Generalizing the theorem for the nonparametric maximum likelihood estimation, we prove the existence and discreteness of the optimal mixing distribution and provide an algorithm to calculate it. It is demonstrated that with an appropriate choice of the parameter, the proposed method is less prone to overfitting than the maximum likelihood method. We further discuss the connection between the unifying estimation framework and the rate-distortion problem. |
| doi_str_mv | 10.1007/s10994-014-5467-7 |
| format | Article |
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| ispartof | Machine learning, 2015-04, Vol.99 (1), p.119-136 |
| issn | 0885-6125 1573-0565 |
| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Artificial Intelligence Computer Science Control Entropy Joints Kernels Machine learning Mathematical models Maximum likelihood estimation Mechatronics Minimization Natural Language Processing (NLP) Optimization Robotics Simulation and Modeling Statistics Vector quantization |
| title | Entropic risk minimization for nonparametric estimation of mixing distributions |
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