Projective Double Reconstructions Based Dictionary Learning Algorithm for Cross-Domain Recognition

Na Han

doi:10.1109/TIP.2020.3024728

2020-01-01

PRE

Na Han

Jigang Wu

房

房小兆 (Xiaozhao Fang) *

Shaohua Teng

Guoxu Zhou

Xie Shengli (Shengli Xie)

Xuelong Li

DOI：10.1109/TIP.2020.3024728

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摘要

En 中文

Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data cross-reconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l(1) optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the state-of-the-art methods.

Keyword:

Dictionary learning

label consistency

data reconstruction

cross-domain recognition