Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations
نویسندگان
چکیده
منابع مشابه
Declaratively Capturing Local Label Correlations with Multi-Label Trees
The goal of multi-label classification is to predict multiple labels per data point simultaneously. Real-world applications tend to have high-dimensional label spaces, employing hundreds or even thousands of labels. While these labels could be predicted separately, by capturing label correlation we might achieve better predictive performance. In contrast with previous attempts in the literature...
متن کاملMulti-Label Learning with Global and Local Label Correlation
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are sh...
متن کاملMulti-Label Learning by Exploiting Label Correlations Locally
It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances. In real-world tasks, however, different instances may share different label correlations, and few correlations are globally applicable. In this paper, we propose the ...
متن کاملMulti-label classification by exploiting label correlations
Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e Sch...
متن کاملMulti-Label Learning with Weak Label
Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the “full” label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a “partial” set of labels is available. In such cases, the appeara...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2980219