@inproceedings{f6a2b26e503649cbb47f748a964b8b10,
title = "Balson: Bayesian least squares optimization with nonnegative L1-Norm constraint",
abstract = "A Bayesian approach termed the BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the moments of the approximated Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced and implemented. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.",
keywords = "Bayesian learning, Dirichlet distribution, L-norm constraint, Least squares optimization, Sampling method",
author = "Jiyang Xie and Zhanyu Ma and Guoqiang Zhang and Xue, \{Jing Hao\} and Jen-Tzung Chien and Zhiqing Lin and Jun Guo",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 ; Conference date: 17-09-2018 Through 20-09-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/MLSP.2018.8517036",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Nelly Pustelnik and Zheng-Hua Tan and Zhanyu Ma and Jan Larsen",
booktitle = "2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings",
address = "美國",
}