An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network

  • Authors:

  • Furao Shen;Hui Yu;Keisuke Sakurai;Osamu Hasegawa

  • Affiliations:

  • Nanjing University, The State Key Laboratory for Novel Software Technology, Nanjing, People’s Republic of China and Nanjing University, Jiangyin Information Technology Research Institute, N ...;Nanjing University, The State Key Laboratory for Novel Software Technology, Nanjing, People’s Republic of China and Nanjing University, Jiangyin Information Technology Research Institute, N ...;Tokyo Institute of Technology, Imaging Science and Engineering Laboratory, Tokyo, Japan;Tokyo Institute of Technology, Imaging Science and Engineering Laboratory, Tokyo, Japan

  • Venue:
  • Neural Computing and Applications - Special Issue on ICONIP2009
  • Year:
  • 2011

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Abstract

An incremental online semi-supervised active learning algorithm, which is based on a self-organizing incremental neural network (SOINN), is proposed. This paper describes improvement of the two-layer SOINN to a single-layer SOINN to represent the topological structure of input data and to separate the generated nodes into different groups and subclusters. We then actively label some teacher nodes and use such teacher nodes to label all unlabeled nodes. The proposed method can learn from both labeled and unlabeled samples. It can query the labels of some important samples rather than selecting the labeled samples randomly. It requires neither prior knowledge, such as the number of nodes, nor the number of classes. It can automatically learn the number of nodes and teacher vectors required for a current task. Moreover, it can realize online incremental learning. Experiments using artificial data and real-world data show that the proposed method performs effectively and efficiently.