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Description: PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
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Description: 使用uve算法进行特征波段提取，并建立预测模型-Bands with uve algorithm to extract features and build predictive models
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Size: 1024 |
Author: 李静 |
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Description: 统计算法 里面很多经典的多元统计算法程序 PLS，PCR GA PCR-UVE PLS-UVE UVE-CV PLS-GA MLR LWR 人工神经网络 SPA LSS等-Statistical algorithms, there are many classic multivariate statistical algorithm PLS, PCR GA PCR-UVE PLS-UVE UVE-CV PLS-GA MLR LWR artificial neural network SPA LSS
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Author: 木华 |
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Description: 对一维光谱实现偏最小二乘回归进行UVE变量选择-UVE variable selection on the spectrum
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Size: 1024 |
Author: 王同晖 |
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Description: 五组在matlab中应用的小程序：（1）carspls and carsplslda，竞争性自适应重加权算法（cars）筛选特征变量，并建立PLS定量或定性模型（2）mcuvepls 蒙特卡罗抽样无关信息消除后，建立PLS定量和定性模型（3）osc，正交信号校正的多变量数据处理方法（4）plot cars-lda-mcs,表征cars-lda-mcs的结果（5）demo 对以上carspls and carsplslda，mcuvepls 方法在matlab中实现举例演示-Five groups codes in matlabapplication: (1) carspls and carsplslda, competitive adaptive re-weighted algorithm (cars) for characteristic variables selection, and the establishment of PLS quantitative or qualitative model (2) mcuvepls， Monte Carlo sampling and the uninformative variables elimination （UVE）， and the establishment of quantitative and qualitative PLS model (3) Orthagnal signal correction（osc）, multivariate data processing method (4) plot cars-lda-mcs, characterization results of cars-lda-mcs (5) the demo about carspls and carsplslda, mcuvepls method implementation in matlab
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Author: 詹雪 |
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Description: UVE是基于PLS回归系数建立的一种波长选择方法，这种方法的基本思想是将回归系数作为波长重要性的衡量指标。该方法将一定变量数目的随机变量矩阵加入到光谱矩阵中，然后通过蒙特卡洛交互验证或者传统的交互验证建立PLS模型，通过计算PLS回归系数平均值与标准偏差的比值，选取有效光谱信息。-UVE was established based on PLS regression coefficient of a wavelength selection method, the basic idea of this method is that the regression coefficient as a measure of the importance of wavelength.The method to add a certain variable number of random variable matrix to the spectral matrix, and then through the monte carlo interaction validation or traditional interaction validation PLS model is set up, by calculating the average PLS regression coefficients and the ratio of standard deviation, effective spectral information.
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Author: 詹映 |
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Description: 基于偏最小二乘回归的matlab中无信息变量消除算法的特征选择(Feature Selection of No Information Variable Elimination Algorithm in Matlab Based on Partial Least Squares Regression)
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Size: 2048 |
Author: 金银花露水 |
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Description: this is the pls method used uve.
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Size: 21504 |
Author: JUNJUNWU |
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Description: uve算法提取光谱特征波长，matlab例程，结合了pls(Uve algorithm to extract spectral characteristic wavelength, matlab routine, combined with PLS)
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Size: 500736 |
Author: snoworday |
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Description: UVE提取特征，数据为高光谱数据，感兴趣区域数据，最后一列为标签(Uve extracts features, hyperspectral data, region of interest data, and the last column is label)
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Size: 756736 |
Author: 叶大白 |
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