关键词: 迁移学习, 谱聚类, 正则化

Abstract:  Traditional clustering algorithm usually has poor clustering performance in the cases where the target data are fairly distorted by noise. In order to address such challenge, based on the classic spectral clustering (SC) algorithm, and by using the strategy of transfer learning, this paper proposes the transfer spectral clustering algorithm based on inter-domain F-norm regularization (TSC-IDFR). For the data in the target domain, TSC-IDFR firstly selects the referenced examples, of which the sampling size is the same as the data size in the target domain, from the source domain (historical data) by means of the principle of the Kth nearest neighbor. Then, in terms of the mechanism of inter-domain F-norm regularization, the matrix composed of the spectral eigenvectors of the selected referenced examples from the source domain is used to assist the spectral clustering on the target data. As such, TSC-IDFR successfully resolves the clustering on the target data set (target domain) even if it contains much noise. The effectiveness of the proposed algorithm has been demonstrated by experimental studies on both synthetic and real data sets.

Key words: transfer learning, spectral clustering, regularization