nlpca(pcaMethods)
nlpca()所属R语言包：pcaMethods
Non-linear PCA
非线性PCA
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Neural network based non-linear PCA
基于神经网络的非线性PCA
用法----------Usage----------
functionsPerLayer, weightDecay=0.001, weights,
参数----------Arguments----------
参数：Matrix
matrix — Preprocessed data with the variables in columns and observations in rows. The data may contain missing values, denoted as NA
matrix - 预处理数据的变量列和行的意见。数据可能包含缺失值，记为NA
参数：nPcs
numeric – Number of components to estimate. The preciseness of the missing value estimation depends on thenumber of components, which should resemble the internal structure of the data.
numeric - 组件的数量估计。严谨的缺失值估计依赖于数量写的组件，它应该像数据的内部结构。
参数：maxSteps
numeric – Number of estimation steps. Default is based on a generous rule of thumb.
numeric - 数估计步骤。默认的基础上慷慨的拇指规则。
参数：unitsPerLayer
The network units, example: c(2,4,6) for two input units 2feature units (principal components), one hidden layer fornon-linearity and three output units (original amount ofvariables).
网络单位，例如：C为两个输入单位2feature单位（主要成分），一个隐藏层fornon线性和三个输出单位（原金额ofvariables）（2,4,6）。
参数：functionsPerLayer
The function to apply at each layer eg. c("linr", "tanh", "linr")
该功能适用于每一层如。 C（“linr”，“双曲正切”，“linr”）
参数：weightDecay
Value between 0 and 1.
0和1之间的值。
参数：weights
Starting weights for the network. Defaults to uniform random values but can be set specifically to make algorithm deterministic.
起重量为网络。默认统一的随机值，但可以设置专门确定性算法。
参数：verbose
boolean – nlpca prints the number of steps and warning messages if set to TRUE. Default is interactive().
boolean - nlpca打印的步骤和警告消息如果设置为TRUE。默认是交互式（）。
参数：...
Reserved for future use. Not passed on anywhere. </table>
保留供将来使用。没有通过任何地方。 </ TABLE>
Details
详情----------Details----------
Artificial Neural Network (MLP) for performing non-linear PCA. Non-linear PCA is conceptually similar to classical PCA but theoretically quite different. Instead of simply decomposing our matrix (X) to scores (T) loadings (P) and an error (E) we train a neural network (our loadings) to find a curve through the multidimensional space of X that describes a much variance as possible. Classical ways of interpreting PCA results are thus not applicable to NLPCA since the loadings are hidden in the network. However, the scores of components that lead to low cross-validation errors can still be interpreted via the score plot. Unfortunately this method depend on slow iterations which currently are implemented in R only making this method extremely slow. Furthermore, the algorithm does not by itself decide when it
人工神经网络（MLP），用于执行非线性主成分分析。非线性PCA的经典PCA的概念是类似的，但理论上完全不同。而不是简单地分解我们的矩阵（X）以分数（T）的载荷（P）和错误（e）我们训练一个神经网络（负荷）通过多维空间的X找到一个描述尽可能得多变异的曲线。古典的方式解释主成分分析结果，因此并不适用于NLPCA以来隐藏在网络负荷。然而，低交叉验证错误，导致组件的得分仍然可以通过得分图被解释。不幸的是，这种方法依赖于目前仅在研发实施这种方法非常缓慢的缓慢迭代。此外，该算法不决定本身时
值----------Value----------
Standard PCA result object used by all PCA-basedmethods of this package. Contains scores, loadings, data meanand more. See
使用所有PCA这个包的，basedmethods标准PCA结果对象。包含分数，载荷，数据meanand。见
作者（S）----------Author(s)----------
Based on a matlab script by Matthias Scholz and ported to
R by Henning Redestig
参考文献----------References----------
Kopkaand Joachim Selbig. Non-linear PCA: a missing
举例----------Examples----------
data(helix)
helixNA <- helix
## not a single complete observation[＃不是一个完整观察]
helixNA <- t(apply(helix, 1, function(x) { x[sample(1:3, 1)] <- NA; x}))
## 50 steps is not enough, for good estimation use 1000[＃50步骤是不够的，良好的估计使用1000]
helixNlPca <- pca(helixNA, nPcs=1, method="nlpca", maxSteps=50)
fittedData <- fitted(helixNlPca, helixNA)
plot(fittedData[which(is.na(helixNA))], helix[which(is.na(helixNA))])
## compared to solution by Nipals PCA which cannot extract non-linear patterns[＃相比Nipals PCA的解决方案，它可以提取非线性模式]
helixNipPca <- pca(helixNA, nPcs=2)
fittedData <- fitted(helixNipPca)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 10:50:16
