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A convergence algorithm for graph co-regularized transfer learning
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DOI：10.1007/s11432-020-3526-4.png)
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Abstract
En 中文
Transfer learning is an important technology in addressing the problem that labeled data in a target domain are difficult to collect using extensive labeled data from the source domain. Recently, an algorithm named graph co-regularized transfer learning (GTL) has shown a competitive performance in transfer learning. However, its convergence is affected by the used approximate scheme, degenerating learned results. In this paper, after analyzing convergence conditions, we propose a novel update rule using the multiplicative update rule and develop a new algorithm named improved GTL (IGTL) with a strict convergence guarantee. Moreover, to prove the convergence of our method, we design a special auxiliary function whose value is intimately related to that of the objective function. Finally, the experimental results on the synthetic dataset and two real-world datasets confirm that the proposed IGTL is convergent and performs better than the compared methods.
Keywords:
transfer learning
convergence analysis
non-negative matrix factorization
multiplicative update algorithm
optimization
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