Cross-domain structure preserving projection for heterogeneous domain adaptation
作者：
Highlights：
• Extending locality preserving projection (LPP) to multi-domain scenarios.
• Proposing a cross-domain structure preserving projection (CDSPP) algorithm for heterogeneous domain adaptation (HDA).
• A progressive pseudo-labelling strategy is proposed for semi-supervised HDA.
• A new benchmark for HDA with significantly more classes than the existing ones is presented.
• Extensive experiments are conducted to validate the effectiveness of our proposed method for both supervised and semi-supervised HDA.
摘要
•Extending locality preserving projection (LPP) to multi-domain scenarios.•Proposing a cross-domain structure preserving projection (CDSPP) algorithm for heterogeneous domain adaptation (HDA).•A progressive pseudo-labelling strategy is proposed for semi-supervised HDA.•A new benchmark for HDA with significantly more classes than the existing ones is presented.•Extensive experiments are conducted to validate the effectiveness of our proposed method for both supervised and semi-supervised HDA.
论文关键词：Heterogeneous domain adaptation,Cross-domain projection,Image classification,Text classification
论文评审过程：Received 25 December 2020, Revised 25 September 2021, Accepted 2 October 2021, Available online 9 October 2021, Version of Record 16 October 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108362
