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看到这段才终于明白了矩阵特征值分解的真正含义啊！！！
What Does This Have to Do with the Articles Matrix?
So far, what you have is a matrix of articles with word counts. The goal is to factorize
this  matrix,  which  means  finding  two  smaller  matrices  that  can  be  multiplied
together to reconstruct this one. The two smaller matrices are:
The features matrix
This matrix has a row for each feature and a column for each word. The values
indicate how important a word is to a feature. Each feature should represent a
theme that emerged from a set of articles, so you might expect an article about a
new TV show to have a high weight for the word “television.”
The weights matrix
This matrix maps the features to the articles matrix. Each row is an article and
each column is a feature. The values state how much each feature applies to each
article..