v-soft margin multi-task learning logistic regression

摘要

Coordinate descent (CD) is an effective method for large scale classification problems with simple operations and fast convergence speed. In this paper, inspired by v-soft margin support vector machine and multi-task learning support vector machine for classification, a novel v-soft margin multi-task learning logistic regression (v-SMMTL-LR) for pattern classification is proposed to improve the generalization performance of logistic regression (LR). The dual of v-SMMTL-LR can be viewed as dual coordinate descent (CDdual) problem with equality constraint and then its large scale classification method named v-SMMTL-LR-CDdual is developed. The proposed method v-SMMTL-LR-CDdual can maximize the between-class margin and effectively improve the generalization performance of LR for large scale multi-task learning scenarios. Experimental results show that the proposed method v-SMMTL-LR-CDdual is effective for large scale multi-task datasets or comparatively high dimensional multi-task datasets and that it is competitive to other related single-task and multi-task learning algorithms.