nmfsc(fabia)
nmfsc()所属R语言包：fabia
Non-negative Sparse Matrix Factorization
非负稀疏矩阵分解
译者：生物统计家园网 机器人LoveR
描述----------Description----------
nmfsc: R implementation of nmfsc.
nmfsc：R的nmfsc实施。
用法----------Usage----------
nmfsc(X,p=5,cyc=100,sL=0.6,sZ=0.6)
参数----------Arguments----------
参数：X
the data matrix.
数据矩阵。
参数：p
number of hidden factors = number of biclusters; default = 5.
隐性因素数=的biclusters;默认值= 5。
参数：cyc
maximal number of iterations; default = 100.
最大迭代次数，默认为100。
参数：sL
sparseness loadings; default = 0.6.
稀疏的负荷;默认值= 0.6。
参数：sZ
sparseness factors; default = 0.6.
稀疏的因素;默认值= 0.6。
Details
详情----------Details----------
Non-negative Matrix Factorization represents positive matrix X by positive matrices L and Z that are sparse.
非负矩阵分解正定矩阵X正定矩阵L和Z这是稀疏。
Objective for reconstruction is Euclidean distance and sparseness constraints.
重建的目的是欧几里德距离和稀疏约束。
Essentially the model is the sum of outer products of vectors:
模型本质上是向量外产品的总和：
where the number of summands p is the number of biclusters. The matrix factorization is
加数p是的biclusters数量。矩阵分解
Here λ_i are from R^n, z_i from R^l, L from R^{n \times p}, Z from R^{p \times l}, and X from R^{n \times l}.
这里λ_iR^n，z_iR^l，LR^{n \times p}，ZR^{p \times l}和XR^{n \times l}。
If the nonzero components of the sparse vectors are grouped together then the outer product results in a matrix with a nonzero block and zeros elsewhere.
如果稀疏向量的非零组件被组合在一起，然后在同一个非零块矩阵和零别处外产品的结果。
The model selection is performed by a constraint optimization according to Hoyer, 2004. The Euclidean distance (the Frobenius norm) is minimized subject to sparseness and non-negativity constraints.
模型的选择是根据2004年霍耶，约束优化。最小欧氏距离（Frobenius范）受稀疏和非负约束。
Model selection is done by gradient descent on the Euclidean objective and thereafter projection of single vectors of L and single vectors of Z to fulfill the sparseness and non-negativity constraints.
模式的选择是通过欧几里德的目标，其后L和单向量单一向量的投影梯度下降Z履行稀疏和非负约束。
The projection minimize the Euclidean distance to the original vector given an l_1-norm and an l_2-norm and enforcing non-negativity.
投影减少到原来的向量给予l_1规范和l_2的规范和执行非负欧几里德距离。
The projection is a convex quadratic problem which is solved iteratively where at each iteration at least one component is set to zero. Instead of the l_1-norm a sparseness measurement is used which relates the l_1-norm to the l_2-norm.
投影是一个凸二次其中至少一个组件，在每一次迭代设置为零，这是解决反复的问题。而不是l_1-规范使用1稀疏测量涉及l_1，规范l_2-范。
The code is implemented in R.
河中实现代码
值----------Value----------
参数：
object of the class Factorization. Containing LZ (estimated noise free data L Z), L (loadings L), Z (factors Z), U (noise X-LZ), X (data X).
对象类Factorization。含有LZ（估计无噪音的数据L Z）L（负荷L）Z（因素Z）U （噪音X-LZ）X（数据X）。
作者（S）----------Author(s)----------
Sepp Hochreiter
参考文献----------References----------
‘Non-negative Matrix Factorization with Sparseness Constraints’, Journal of Machine Learning Research 5:1457-1469, 2004.
‘Algorithms for non-negative matrix factorization’, In Advances in Neural Information Processing Systems 13, 556-562, 2001.
参见----------See Also----------
fabia, fabias, fabiap, fabi, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, plot, extractPlot, extractBic, plotBicluster, Factorization, projFuncPos, projFunc, estimateMode, makeFabiaData, makeFabiaDataBlocks, makeFabiaDataPos, makeFabiaDataBlocksPos, matrixImagePlot, summary, show, showSelected, fabiaDemo, fabiaVersion
fabia，fabias，fabiap，fabi，fabiasp，mfsc，nmfdiv，nmfeu，nmfsc，plot，extractPlot，extractBic，plotBicluster，Factorization，projFuncPos，projFunc，estimateMode ，makeFabiaData，makeFabiaDataBlocks，makeFabiaDataPos，makeFabiaDataBlocksPos，matrixImagePlot，summary，show，showSelected fabiaDemo，fabiaVersion
举例----------Examples----------
#---------------[---------------]
# TEST[试验]
#---------------[---------------]
dat <- makeFabiaDataBlocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)
X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)
resEx <- nmfsc(X,3,30,0.6,0.6)
## Not run: [＃无法运行：]
#---------------[---------------]
# DEMO[演示]
#---------------[---------------]
dat <- makeFabiaDataBlocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)
X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)
resToy <- nmfsc(X,13,100,0.6,0.6)
extractPlot(resToy,ti="NMFSC",Y=Y)
## End(Not run)[＃结束（不运行）]
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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。
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发表于 2012-2-25 17:30:51
