Adaptive-weighting discriminative regression for multi-view classification

作者：

Highlights：

• We address the multi-view feature learning problem with a novel discriminative regression based framework, which maps the multi-view data to a unified low-dimensional discriminative subspace.

• We introduce a set of learnable weight parameters that can be merged into the transformation matrix, such that the correlative and the complementary information of the original views can be preserved in the projected subspace simultaneously.

• We design an efficient iterative optimization algorithm with closed-form solution to update the learnable parameters during each iteration, which expresses a remarkable convergence speed in extensive experiments.

摘要

•We address the multi-view feature learning problem with a novel discriminative regression based framework, which maps the multi-view data to a unified low-dimensional discriminative subspace.•We introduce a set of learnable weight parameters that can be merged into the transformation matrix, such that the correlative and the complementary information of the original views can be preserved in the projected subspace simultaneously.•We design an efficient iterative optimization algorithm with closed-form solution to update the learnable parameters during each iteration, which expresses a remarkable convergence speed in extensive experiments.

论文关键词：Multi-view learning,Supervised learning,Classification

论文评审过程：Received 27 July 2018, Revised 14 October 2018, Accepted 17 November 2018, Available online 22 November 2018, Version of Record 26 November 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.015