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Open AccessArticle

A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation

Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
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Sensors 2020, 20(16), 4367; https://doi.org/10.3390/s20164367
Received: 13 July 2020 / Revised: 30 July 2020 / Accepted: 3 August 2020 / Published: 5 August 2020
(This article belongs to the Section Sensing and Imaging)
In a real-world application, the images taken by different cameras with different conditions often incur illumination variation, low-resolution, different poses, blur, etc., which leads to a large distribution difference or gap between training (source) and test (target) images. This distribution gap is challenging for many primitive machine learning classification and clustering algorithms such as k-Nearest Neighbor (k-NN) and k-means. In order to minimize this distribution gap, we propose a novel Subspace based Transfer Joint Matching with Laplacian Regularization (STJML) method for visual domain adaptation by jointly matching the features and re-weighting the instances across different domains. Specifically, the proposed STJML-based method includes four key components: (1) considering subspaces of both domains; (2) instance re-weighting; (3) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; (4) preserving the original similarity of data points by using Laplacian regularization. Experiments on three popular real-world domain adaptation problem datasets demonstrate a significant performance improvement of our proposed method over published state-of-the-art primitive and domain adaptation methods. View Full-Text
Keywords: domain adaptation; unsupervised discriminant analysis; transfer learning; classification; feature learning; instance re-weighting domain adaptation; unsupervised discriminant analysis; transfer learning; classification; feature learning; instance re-weighting
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Sanodiya, R.K.; Yao, L. A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation. Sensors 2020, 20, 4367.

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