Robust and graph regularised non-negative matrix factorisation for heterogeneous co-transfer clustering Online publication date: Fri, 14-Dec-2018
by Yu Ma; Zhikui Chen; Xiru Qiu; Liang Zhao
International Journal of Computational Science and Engineering (IJCSE), Vol. 18, No. 1, 2019
Abstract: Transferring learning is proposed to tackle the problem where target instances are scarce to train an accurate model. Most existing transferring learning algorithms are designed for supervised learning and cannot obtain transferring results on multiple heterogeneous domains simultaneously. Moreover, the performance of transfer learning can be seriously degraded with the appearance of noises and corruptions. In this paper, a robust non-negative collective matrix factorisation model is proposed for heterogeneous co-transfer clustering which introduces the error matrices to capture the sparsely distributed noises. The heterogeneous clustering tasks are handled simultaneously and the graph regularisation is enforced on the collective matrix factorisation model to keep the intrinsic geometric structure of different domains. Experiment results on the real-world dataset show the proposed algorithm outperforms the baselines.
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