可解释性
计算机科学
完备性(序理论)
人工智能
代表(政治)
缺少数据
机器学习
理论计算机科学
互补性(分子生物学)
利用
特征学习
背景(考古学)
依赖关系(UML)
数据挖掘
数学
数学分析
古生物学
政治
生物
法学
遗传学
政治学
计算机安全
作者
Changqing Zhang,Zongbo Han,Yajie Cui,Huazhu Fu,Joey Tianyi Zhou,Qinghua Hu
出处
期刊:Neural Information Processing Systems
日期:2019-01-01
卷期号:32: 557-567
被引量:66
摘要
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets). In this framework, we first give a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the latent representation learned from our algorithm. To achieve the completeness, the task of learning latent multi-view representation is specifically translated to degradation process through mimicking data transmitting, such that the optimal tradeoff between consistence and complementarity across different views could be achieved. In contrast with methods that either complete missing views or group samples according to view-missing patterns, our model fully exploits all samples and all views to produce structured representation for interpretability. Extensive experimental results validate the effectiveness of our algorithm over existing state-of-the-arts.
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