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Graph Domain Adaptation for Semi-Supervised Learning
- Author(s)
- 박성홍
- Advisor
- 신현정
- Department
- 일반대학원 인공지능학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-08
- Language
- eng
- Keyword
- Domain Adaptation; Graph; Semi-Supervised Learning
- Alternative Abstract
- Graphs are used in wide range of domains, such as bioinformatics, text analytics, and social network in that graphs can capture associations of various sources of data while managing large amount of data. In real-world, graphs exist in the form of multiple data. For example, there are longitudinal data generated according to the passage of time, multi-modal data including various relational information, or heterogeneous data from different domains. Therefore, when applying the machine learning algorithm to graphs, learning multiple data together rather than single data has the advantage of utilizing more information. However, learning multiple data in a simple way can be rather detrimental due to the different properties of data and domains. For this reason, a process of transforming multiple data to be regarded as one set is required, and this process should be subdivided according to data types. In this dissertation, a domain adaptation-based framework for semi-supervised learning is proposed, which can learn multiple graph data in an integrated way. The proposed framework divides data types into three cases for efficient learning and includes methods suitable for each: mutual adaptation for heterogeneous data, prospective adaptation for longitudinal data, and multiplex adaptation for multi-modal data. Each method enables comprehensive data integration and learning, and some real-world applications such as Alzheimer’s disease conversion prediction and historical faction identification can also be successfully performed.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 4. Theses(Ph.D)
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