arXiv Open Access 2017

An Empirical Bayes Approach for High Dimensional Classification

Yunbo Ouyang Feng Liang

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Abstrak

We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification. In theory, we build a bridge to connect the estimation error of the mean difference and the misclassification error, also provide sufficient conditions of sub-optimal classifiers and optimal classifiers. In implementation, a variational Bayes algorithm is developed to compute the posterior efficiently and could be parallelized to deal with the ultra-high dimensional case.

Topik & Kata Kunci

stat.ML stat.ME

Penulis (2)

Yunbo Ouyang

Feng Liang

Format Sitasi

APA MLA BibTeX

Ouyang, Y., Liang, F. (2017). An Empirical Bayes Approach for High Dimensional Classification. https://arxiv.org/abs/1702.05056

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓