Title of article :
GENERALIZED KERNEL-BASED RANDOM K-SAMPLESETS METHOD FOR TRANSFER LEARNING
Author/Authors :
TAHMORESNEZHAD, J. shiraz university - School of Electrical and Computer Engineering, شيراز, ايران , HASHEMI, S. shiraz university - School of Electrical and Computer Engineering, شيراز, ايران
From page :
193
To page :
207
Abstract :
Transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. Feature selection for transfer learning (f-MMD) is a simple and effective transfer learning method, which tackles the domain shift problem. f-MMD has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive performance of f-MMD is challenged by the application domains with large number of examples and features, and ii) f-MMD considers the domain shift problem in fully unsupervised manner. In this paper, we propose a new approach to break down the large initial set of samples into a number of small-sized random subsets, called samplesets. Moreover, we present a feature weighting and instance clustering approach, which categorizes the original feature samplesets into the variant and invariant features. In domain shift problem, invariant features have a vital role in transferring knowledge across domains. The proposed method is called RAkET (RAndom k samplesETs), where k is a parameter that determines the size of the samplesets. Empirical evidence indicates that RAkET manages to improve substantially over f-MMD, especially in domains with large number of features and examples. We evaluate RAkET against other well-known transfer learning methods on synthetic and real world datasets.
Keywords :
Transfer learning , unsupervised domain adaptation , random samplesets , feature weighting , instance clustering
Journal title :
Iranian Journal of Science and Technology :Transactions of Electrical Engineering
Journal title :
Iranian Journal of Science and Technology :Transactions of Electrical Engineering
Record number :
2596400
Link To Document :
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