Affordable Access

deepdyve-link
Publisher Website

Latent Elastic-Net Transfer Learning.

Authors
  • Han, Na
  • Wu, Jigang
  • Fang, Xiaozhao
  • Xie, Shengli
  • Zhan, Shanhua
  • Xie, Kan
  • Li, Xuelong
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Nov 15, 2019
Identifiers
DOI: 10.1109/TIP.2019.2952739
PMID: 31751275
Source
Medline
Language
English
License
Unknown

Abstract

Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.

Report this publication

Statistics

Seen <100 times