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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: 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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Size: 4096 |
Author: 詹雪 |
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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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