In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existin...In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.展开更多
Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image fro...Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.展开更多
The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressiv...The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressive sensing(CS)and super-resolution convolutional neural network(SRCNN)techniques are combined to capture transparent objects.With the proposed method,the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction.The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object.However,the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly.The developed algorithm locates the deformities in the resultant images and improves the image quality.Additionally,the inclusion of compressive sensing minimizes the measurements required for reconstruction,thereby reducing image post-processing and hardware requirements during network training.The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index(SSIM)value of 0.2 to 0.53.In this work,we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.展开更多
文摘In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.
基金the National Natural Science Foundation of China(Nos.11401318 and 11671004)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.15KJB110018)the Scientific Research Foundation of NUPT(No.NY214023)
文摘Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.
基金This research was funded by the Ministry of Higher Education,Malaysia(Grant No.Grant FRGS/1/2020/ICT02/MUSM/02/1).
文摘The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination.In this paper,both compressive sensing(CS)and super-resolution convolutional neural network(SRCNN)techniques are combined to capture transparent objects.With the proposed method,the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction.The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object.However,the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly.The developed algorithm locates the deformities in the resultant images and improves the image quality.Additionally,the inclusion of compressive sensing minimizes the measurements required for reconstruction,thereby reducing image post-processing and hardware requirements during network training.The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index(SSIM)value of 0.2 to 0.53.In this work,we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.
