Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Label Inference Algorithm Considering Vertex Importance in Semi-Supervised Learning

준지도 학습에서 꼭지점 중요도를 고려한 레이블 추론

  • 오병화 (서강대학교 컴퓨터공학과) ;
  • 양지훈 (서강대학교 컴퓨터공학과) ;
  • 이현진 (한국전자통신연구원 방송통신미디어연구소)
  • Received : 2015.07.23
  • Accepted : 2015.10.02
  • Published : 2015.12.15
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Abstract

Abstract Semi-supervised learning is an area in machine learning that employs both labeled and unlabeled data in order to train a model and has the potential to improve prediction performance compared to supervised learning. Graph-based semi-supervised learning has recently come into focus with two phases: graph construction, which converts the input data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. The inference is based on the smoothness assumption feature of semi-supervised learning. In this study, we propose an enhanced label inference algorithm by incorporating the importance of each vertex. In addition, we prove the convergence of the suggested algorithm and verify its excellence.

준지도 학습은 기계 학습의 한 분야로서, 레이블된 데이터와 레이블되지 않은 데이터 모두를 사용하여 모델을 학습함으로써 지도 학습에 비해 예측 정확도를 높일 수 있다. 최근 각광받고 있는 그래프 기반 준지도 학습은 입력 데이터를 그래프의 형태로 변환하는 그래프 구축 단계와 이를 사용하여 레이블되지 않은 데이터의 레이블을 예측하는 레이블 추론 단계로 나뉜다. 이 추론은 준지도 학습에서의 평활도 가정을 기본으로 한다. 본 연구에서는 추가로 각 꼭지점 중요도를 결합함으로써 개선된 레이블 추론 알고리즘을 제안한다. 이와 함께 알고리즘의 수렴성을 증명하고, 또한 실험을 통해 알고리즘의 우수성을 검증하였다.

Keywords

Acknowledgement

Grant : 효과적인 데이터 분석을 위한 준지도 학습 알고리즘 개발, 상황인지형 Tele-Screen 시스템 기술 개발

Supported by : 한국연구재단, 정보통신기술진흥센터

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