计算机科学
水准点(测量)
分类
班级(哲学)
集合(抽象数据类型)
机器学习
钥匙(锁)
人工智能
过程(计算)
开放集
层次Dirichlet过程
数据挖掘
文本分类
决策树
选择(遗传算法)
潜在Dirichlet分配
数学
主题模型
计算机安全
大地测量学
离散数学
程序设计语言
地理
操作系统
作者
Chuanxing Geng,Songcan Chen
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2020-03-04
卷期号:34 (1): 192-204
被引量:36
标识
DOI:10.1109/tkde.2020.2978199
摘要
In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.
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