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Information Journal Paper

Title

Prediction Error Minimization of Image Classification Models via Sparse Coding and Domain Adaptation

Pages

  231-242

Abstract

 domain adaptation can transfer knowledge from a training set (source domain) to a test set (target domain), promoting the performance of the model learned from the training set. In addition, sparse coding makes the learned model more succinct and easy to manipulate. However, the existence of the distribution mismatch across the source and target domains reduce the performance of model. In this paper, we propose an unsupervised domain adaptation model to minimize the prediction error of image classification. Sample reweighting is utilized to handle redundant and useless information of source data in the new representation. Moreover, the difference of the conditional distributions across the source and target domains is reduced along with the subspace alignment. Our proposed approach learns a sparse domain-invariant classifier in a latent subspace with preserving the structure of the input data. Extensive experiments demonstrate that our proposed approach shows 4. 49% improvement in classification accuracy on real-world datasets compared to state-of-the-art machine learning and domain adaptation methods.

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    APA: Copy

    SHERAFATI, F., & Tahmoresnezhad, J.. (2020). Prediction Error Minimization of Image Classification Models via Sparse Coding and Domain Adaptation. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 50(1 (91) ), 231-242. SID. https://sid.ir/paper/388274/en

    Vancouver: Copy

    SHERAFATI F., Tahmoresnezhad J.. Prediction Error Minimization of Image Classification Models via Sparse Coding and Domain Adaptation. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING[Internet]. 2020;50(1 (91) ):231-242. Available from: https://sid.ir/paper/388274/en

    IEEE: Copy

    F. SHERAFATI, and J. Tahmoresnezhad, “Prediction Error Minimization of Image Classification Models via Sparse Coding and Domain Adaptation,” TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 50, no. 1 (91) , pp. 231–242, 2020, [Online]. Available: https://sid.ir/paper/388274/en

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