Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process, we built a regularized regression model using Truncated Singular Value Decomposition （T...Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process, we built a regularized regression model using Truncated Singular Value Decomposition （TSVD）. An earth-rock dam in China is presented and discussed as an example. The analysis consists of three steps： multicollinearity detection, regularization pa- rameter selection, and crack opening modeling and forecasting. Generalized Cross-Validation （GCV） function and L-curve criterion are both adopted in the regularization parameter selection. Partial Least-Squares Regression （PLSR） and stepwise regression are also included for comparison. The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models. However, no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem. Both fitting accuracy and coefficients＇ reasonability should be considered when evaluating the mode/reliability.展开更多
截断奇异值(truncated singular value decomposition,TSVD)法通过截掉病态观测方程系数矩阵的小奇异值来改善模型的病态性,提高参数估值的稳定性和精度。然而,截除小奇异值后,改变了观测方程的结构,不仅参数估值有偏,残差估值也是有偏...截断奇异值(truncated singular value decomposition,TSVD)法通过截掉病态观测方程系数矩阵的小奇异值来改善模型的病态性,提高参数估值的稳定性和精度。然而,截除小奇异值后,改变了观测方程的结构,不仅参数估值有偏,残差估值也是有偏的;因此,其单位权方差不能用传统的估计公式计算。针对此,导出了TSVD正则化解的单位权方差无偏公式,并以第一类Fredholm积分方程和病态测边网为算例验证了公式的正确性。展开更多
提出了一种有效的盲检测算法来识别图像复制区域伪造。该算法采用截尾奇异值分解(truncated sin-gular value decomposition,TSVD)变换来处理图像块数据,并对图像块进行相似性匹配检测。实验结果表明,本算法具有较强的检测能力,能够有...提出了一种有效的盲检测算法来识别图像复制区域伪造。该算法采用截尾奇异值分解(truncated sin-gular value decomposition,TSVD)变换来处理图像块数据,并对图像块进行相似性匹配检测。实验结果表明,本算法具有较强的检测能力,能够有效抵抗多种修饰操作,如JPEG有损压缩、高斯模糊、高斯白噪声污染等。展开更多
基金Supported by the Research Project of Department of Water Resources of Zhejiang Province of China (No. RB1010)
文摘Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process, we built a regularized regression model using Truncated Singular Value Decomposition （TSVD）. An earth-rock dam in China is presented and discussed as an example. The analysis consists of three steps： multicollinearity detection, regularization pa- rameter selection, and crack opening modeling and forecasting. Generalized Cross-Validation （GCV） function and L-curve criterion are both adopted in the regularization parameter selection. Partial Least-Squares Regression （PLSR） and stepwise regression are also included for comparison. The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models. However, no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem. Both fitting accuracy and coefficients＇ reasonability should be considered when evaluating the mode/reliability.
文摘截断奇异值(truncated singular value decomposition,TSVD)法通过截掉病态观测方程系数矩阵的小奇异值来改善模型的病态性,提高参数估值的稳定性和精度。然而,截除小奇异值后,改变了观测方程的结构,不仅参数估值有偏,残差估值也是有偏的;因此,其单位权方差不能用传统的估计公式计算。针对此,导出了TSVD正则化解的单位权方差无偏公式,并以第一类Fredholm积分方程和病态测边网为算例验证了公式的正确性。
文摘提出了一种有效的盲检测算法来识别图像复制区域伪造。该算法采用截尾奇异值分解(truncated sin-gular value decomposition,TSVD)变换来处理图像块数据,并对图像块进行相似性匹配检测。实验结果表明,本算法具有较强的检测能力,能够有效抵抗多种修饰操作,如JPEG有损压缩、高斯模糊、高斯白噪声污染等。
