Adaptive-weighting discriminative regression for multi-view classification. (April 2019)

• Record Type: Journal Article
• Authors: Yang, Muli; Deng, Cheng; Nie, Feiping
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 236
• Page End: 245
• Publication Date: 2019-04
• Subjects: Multi-view learning -- Supervised learning -- Classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.11.015
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Multi-view data represented by different features have been involved in many machine learning applications. Efficiently exploiting and preserving the correlative yet complementary information in multiple views remains challenging in multi-view learning. Comparing with existing methods that separately cope with each view, we propose a supervised multi-view feature learning framework to handle diverse views with a unified perception. Specifically, we fuse the multi-view data by mapping the concatenation of original features to a discriminative low-dimensional subspace, where the features from different views are adaptively assigned with the learned optimal weights. This strategy can simultaneously preserve the correlative and the complementary information, which is further enhanced to be more discriminative for subsequent classification. An efficient iterative algorithm is devised to optimize the formulated framework with closed-form solutions. Comprehensive evaluations with several state-of-the-art competitors demonstrate the efficiency and the superiority of the proposed method.