Active Learning With Multiple Kernelsopen access
- Authors
- Hong, Song nam; Chae, Jeongmin
- Issue Date
- Jan-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Active learning (AL); Biomedical imaging; Dictionaries; Kernel; Labeling; multiple kernel learning (MKL); online learning; Optimization; Radio frequency; reproducing kernel Hilbert space (RKHS).; Task analysis
- Citation
- IEEE Transactions on Neural Networks and Learning Systems, v.33, no.7, pp.2980 - 2994
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Neural Networks and Learning Systems
- Volume
- 33
- Number
- 7
- Start Page
- 2980
- End Page
- 2994
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1635
- DOI
- 10.1109/TNNLS.2020.3047953
- ISSN
- 2162-237X
- Abstract
- Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this article, we introduce a new research problem, named stream-based active MKL (AMKL), in which a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary for many real-world applications as acquiring a true label is costly or time consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret O(√T) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label requests. Furthermore, we present AMKL with an adaptive kernel selection (named AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary ``on the fly.'' This approach improves the efficiency of active learning and the accuracy of function learning. Via numerical tests with real data sets, we verify the superiority of AMKL-AKS, yielding a similar accuracy performance with OMKL counterpart using a fewer number of labeled data.
- Files in This Item
-
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.