Joint consensus and diversity for multi-view semi-supervised classification
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Joint consensus and diversity for multi-view semi-supervised classification Wenzhang Zhuge1 · Chenping Hou1
· Shaoliang Peng2 · Dongyun Yi1
Received: 9 April 2019 / Revised: 2 July 2019 / Accepted: 16 September 2019 © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019
Abstract As data can be acquired in an ever-increasing number of ways, multi-view data is becoming more and more available. Considering the high price of labeling data in many machine learning applications, we focus on multi-view semi-supervised classification problem. To address this problem, in this paper, we propose a method called joint consensus and diversity for multi-view semi-supervised classification, which learns a common label matrix for all training samples and view-specific classifiers simultaneously. A novel classification loss named probabilistic square hinge loss is proposed, which avoids the incorrect penalization problem and characterizes the contribution of training samples according to its uncertainty. Power mean is introduced to incorporate the losses of different views, which contains the auto-weighted strategy as a special case and distinguishes the importance of various views. To solve the non-convex minimization problem, we prove that its solution can be obtained from another problem with introduced variables. And an efficient algorithm with proved convergence is developed for optimization. Extensive experimental results on nine datasets demonstrate the effectiveness of the proposed algorithm. Keywords Consensus · Diversity · Multi-view · Semi-supervised classification
Editors: Kee-Eung Kim and Jun Zhu.
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Chenping Hou [email protected] Shaoliang Peng [email protected] Wenzhang Zhuge [email protected] Dongyun Yi [email protected]
1
College of Liberal Arts and Science, National University of Defense Technology, Changsha, China
2
College of Computer Science and Electronic Engineering and National Supercomputing Centre in Changsha, Hunan University, Changsha, China
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Machine Learning
1 Introduction With the advent of vast data collection ways, in many real applications of machine learning, pattern recognition, computer vision and data mining, data are easier to have heterogeneous features representing samples from diverse information channels or different feature extractors. For example, in web data, a web page can be represented by its content and link information; in visual data, each image could be described by different descriptors, such as GIST (Oliva and Torralba 2001), HOG (Dalal and Triggs 2005) and SIFT (Lowe 2004). This kind of data is called multi-view data and each representation is referred to a view (Xu et al. 2013). In general, each representation captures specific characteristics of the studied object, therefore, different views have complementary and partly independent information to one another. On the other hand, since these representations describe the same object, there should be consensus information among views. In recent years,
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