Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed dat...Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.展开更多
基金supported by the National Key R&D Program of China(No.2021ZD0112400)the National Natural Science Foundation of China(Grant Nos.62306171,62136005,61976129,62106132,61906114,61906115)+4 种基金the Science and Technology Major Project of Shanxi(No.202201020101006)the Young Scientists Fund of the Natural Science Foundation of Shanxi(Nos.202203021222183,20210302124549)the Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province(Nos.CICIP2023005,CICIP202205)the Science and Technology Innovation Plan for Colleges and Universities of Shanxi Province(2022L296)and Taiyuan University of Science and Technology Doctoral Research Start-up Fund Project(20222106).
文摘Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.
