---
title: "Probit Classifiers with a Generalized Gaussian Scale Mixture Prior"
authors:
  - "Guoqing Liu"
  - "Jianxin Wu"
  - "Suiping Zhou"
doi: "10.5591/978-1-57735-516-8/IJCAI11-232"
url: "https://ijcai.org/proceedings/2011/232"
pdf: "https://ijcai.org/proceedings/2011/0232.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1372-1376"
track: ""
keywords:
---

# Probit Classifiers with a Generalized Gaussian Scale Mixture Prior

Guoqing Liu, Jianxin Wu, Suiping Zhou

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1372-1376. DOI: [10.5591/978-1-57735-516-8/IJCAI11-232](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-232)

## Abstract

Most of the existing probit classifiers are based on sparsity-oriented modeling. However, we show that sparsity is not always desirable in practice, and only an appropriate degree of sparsity is profitable. In this work, we propose a flexible probabilistic model using a generalized Gaussian scale mixture prior that can promote an appropriate degree of sparsity for its model parameters, and yield either sparse or non-sparse estimates according to the intrinsic sparsity of features in a dataset. Model learning is carried out by an efficient modified maximum a posteriori (MAP) estimate. We also show relationships of the proposed model to existing probit classifiers as well as iteratively re-weighted l1 and l2 minimizations. Experiments demonstrate that the proposed method has better or comparable performances in feature selection for linear classifiers as well as in kernel-based classification.