Factors of Variation in Deep Learning In the textbook im reading about Deep Learning, i found :


  When designing features or algorithms for learning features, our goal
  is usually to separate the factors of variation that explain the
  observed data [...]
  the factors are usually not combined by multiplication.


What's the meaning of the bold phrase ?

ps: what's your definition for "factors of variation" ?
Factors of variation are some factors which determine varieties in observed data. If that factors change, the behavior of the data will change.

The bold phrase said these factors are usually independent and one factor does not change by changing the value of the others.