Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, whi...Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.展开更多
【目的】克服Lee-Carter模型中时间因子预测方法在处理人口死亡率数据的非线性和长记忆性方面存在的局限性。【方法】以最小Lp范数误差法作为差分移动平均自回归模型(autoregressive integrated moving average model,ARIMA),基于遗传...【目的】克服Lee-Carter模型中时间因子预测方法在处理人口死亡率数据的非线性和长记忆性方面存在的局限性。【方法】以最小Lp范数误差法作为差分移动平均自回归模型(autoregressive integrated moving average model,ARIMA),基于遗传算法优化的最小二乘支持向量机(least squares support vector machine,LSSVM)和长短期记忆网络(long-short term memory,LSTM)模块权重优化准则,以及结合串联和并联的组合方式,构建了串联模型(series model,SM)和并联模型(parallel model,PM);选取澳大利亚、英国、美国和中国(不包括中国香港、中国澳门和中国台湾地区)4个国家的男性人口死亡率数据进行实证分析,验证组合模型的预测优越性及适用的广泛性。【结果】组合模型的预测效果无论是在时间因子预测方面还是人口死亡率预测方面均优于ARIMA模型,且PM的预测效果优于SM的预测效果。【结论】本研究结果为人口死亡率预测提供新的研究思路与建模方法,也为应对长寿风险提供一定的理论依据。展开更多
混沌压缩感知是一种利用混沌系统实现非线性测量,通过混沌脉冲同步和参数估计技术实现信号重构的压缩感知理论。针对混沌压缩感知重构系统中采用l1范数正则化信号系数导致在信号稀疏水平较高时重构性能急剧下降的问题,利用lp(0 p 1)范...混沌压缩感知是一种利用混沌系统实现非线性测量,通过混沌脉冲同步和参数估计技术实现信号重构的压缩感知理论。针对混沌压缩感知重构系统中采用l1范数正则化信号系数导致在信号稀疏水平较高时重构性能急剧下降的问题,利用lp(0 p 1)范数来正则化信号系数,将重构系统中的非线性约束l1范数最小化问题替换为非线性约束lp范数最小化问题,并提出-正则迭代再加权非线性最小二乘算法进行求解。以Henon混沌为例,研究了频域稀疏信号的重构性能,数值模拟表明lp范数正则化能够准确重构出比l1范数正则化时稀疏水平更高的信号。展开更多
基金the National Natural Science Foundation of China(No.61572033)the Natural Science Foundation of Education Department of Anhui Province of China(No.KJ2015ZD08)the Higher Education Promotion Plan of Anhui Province of China(No.TSKJ2015B14)
文摘Block principle and pattern classification component analysis （BPCA） is a recently developed technique in computer vision In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.
文摘【目的】克服Lee-Carter模型中时间因子预测方法在处理人口死亡率数据的非线性和长记忆性方面存在的局限性。【方法】以最小Lp范数误差法作为差分移动平均自回归模型(autoregressive integrated moving average model,ARIMA),基于遗传算法优化的最小二乘支持向量机(least squares support vector machine,LSSVM)和长短期记忆网络(long-short term memory,LSTM)模块权重优化准则,以及结合串联和并联的组合方式,构建了串联模型(series model,SM)和并联模型(parallel model,PM);选取澳大利亚、英国、美国和中国(不包括中国香港、中国澳门和中国台湾地区)4个国家的男性人口死亡率数据进行实证分析,验证组合模型的预测优越性及适用的广泛性。【结果】组合模型的预测效果无论是在时间因子预测方面还是人口死亡率预测方面均优于ARIMA模型,且PM的预测效果优于SM的预测效果。【结论】本研究结果为人口死亡率预测提供新的研究思路与建模方法,也为应对长寿风险提供一定的理论依据。
文摘混沌压缩感知是一种利用混沌系统实现非线性测量,通过混沌脉冲同步和参数估计技术实现信号重构的压缩感知理论。针对混沌压缩感知重构系统中采用l1范数正则化信号系数导致在信号稀疏水平较高时重构性能急剧下降的问题,利用lp(0 p 1)范数来正则化信号系数,将重构系统中的非线性约束l1范数最小化问题替换为非线性约束lp范数最小化问题,并提出-正则迭代再加权非线性最小二乘算法进行求解。以Henon混沌为例,研究了频域稀疏信号的重构性能,数值模拟表明lp范数正则化能够准确重构出比l1范数正则化时稀疏水平更高的信号。
