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The Kullback-Leibler divergence formulation of the mutual information is predicated on that one is interested in comparing <math>p(x,y)</math> to the fully factorized [[outer product]] <math>p(x) \cdot p(y)</math>. In many problems, such as [[non-negative matrix factorization]], one is interested in less extreme factorizations; specifically, one wishes to compare <math>p(x,y)</math> to a low-rank matrix approximation in some unknown variable <math>w</math>; that is, to what degree one might have

The Kullback-Leibler divergence formulation of the mutual information is predicated on that one is interested in comparing <math>p(x,y)</math> to the fully factorized [[outer product]] <math>p(x) \cdot p(y)</math>. In many problems, such as [[non-negative matrix factorization]], one is interested in less extreme factorizations; specifically, one wishes to compare <math>p(x,y)</math> to a low-rank matrix approximation in some unknown variable <math>w</math>; that is, to what degree one might have

The Kullback-Leibler divergence formulation of the mutual information is predicated on that one is interested in comparing 𝑝(𝑥,𝑦) to the fully factorized outer product 𝑝(𝑥)⋅𝑝(𝑦). In many problems, such as non-negative matrix factorization, one is interested in less extreme factorizations; specifically, one wishes to compare 𝑝(𝑥,𝑦) to a low-rank matrix approximation in some unknown variable 𝑤; that is, to what degree one might have

The Kullback-Leibler divergence formulation of the mutual information is predicated on that one is interested in comparing 𝑝(𝑥,𝑦) to the fully factorized outer product 𝑝(𝑥)⋅𝑝(𝑦). In many problems, such as non-negative matrix factorization, one is interested in '''<font color="#32CD32">less extreme factorizations</font>'''; specifically, one wishes to compare 𝑝(𝑥,𝑦) to a low-rank matrix approximation in some unknown variable 𝑤; that is, to what degree one might have

相互信息的Kullback-Leibler散度公式是基于这样一个结论的：人们有兴趣将𝑝（𝑥，𝑦）与完全分解的外积𝑝（𝑥）进行比较。在许多问题中，例如非负矩阵因式分解，人们对较不极端的因式分解感兴趣；具体地说，人们希望将𝑝（𝑥，𝑦）与某个未知变量𝑤中的低秩矩阵近似进行比较；也就是说，在多大程度上可能会有这样的结果

相互信息的Kullback-Leibler散度公式是基于这样一个结论的：人们会更关注将<math>p(x,y)</math>与完全分解的外积<math>p(x) \cdot p(y)</math>进行比较。在许多问题中，例如非负矩阵因式分解中，人们对'''<font color="#32CD32">较不极端的</font>'''因式分解感兴趣；具体地说，人们希望将<math>p(x,y)</math>与某个未知变量<math>w</math>中的低秩矩阵近似进行比较；也就是说，在多大程度上可能会有这样的结果：

:<math>p(x,y)\approx \sum_w p^\prime (x,w) p^{\prime\prime}(w,y)</math>

:<math>p(x,y)\approx \sum_w p^\prime (x,w) p^{\prime\prime}(w,y)</math>

Alternately, one might be interested in knowing how much more information 𝑝(𝑥,𝑦) carries over its factorization. In such a case, the excess information that the full distribution 𝑝(𝑥,𝑦) carries over the matrix factorization is given by the Kullback-Leibler divergence

Alternately, one might be interested in knowing how much more information 𝑝(𝑥,𝑦) carries over its factorization. In such a case, the excess information that the full distribution 𝑝(𝑥,𝑦) carries over the matrix factorization is given by the Kullback-Leibler divergence

另一方面，人们可能有兴趣知道在因子分解过程中，有多少信息（𝑝（𝑥，𝑦））携带了多少信息。在这种情况下，全分布𝑝（𝑥，𝑦）通过矩阵因子分解所携带的多余信息由Kullback-Leibler散度给出

另一方面，人们可能有兴趣知道在因子分解过程中，有<math>p(x,y)</math>携带了多少信息。在这种情况下，全分布<math>p(x,y)</math>通过矩阵因子分解所携带的多余信息由Kullback-Leibler散度给出

:<math>\operatorname{I}_{LRMA} = \sum_{y \in \mathcal{Y}} \sum_{x \in \mathcal{X}}

:<math>\operatorname{I}_{LRMA} = \sum_{y \in \mathcal{Y}} \sum_{x \in \mathcal{X}}

The conventional definition of the mutual information is recovered in the extreme case that the process <math>W</math> has only one value for <math>w</math>.

The conventional definition of the mutual information is recovered in the extreme case that the process <math>W</math> has only one value for <math>w</math>.

== 变形 Variations ==

== 变形 Variations ==