Discriminative sparse filtering for multi-source image classification

Chao Han, Deyun Zhou, Zhen Yang, Yu Xie, Kai Zhang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.

Original languageEnglish
Article number5868
Pages (from-to)1-17
Number of pages17
JournalSensors
Volume20
Issue number20
DOIs
StatePublished - 2 Oct 2020

Keywords

  • Alternating discriminant optimization
  • Domain adaptation
  • Maximum mean discrepancy
  • Sparse filtering

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