Consider a text classification problem: categorize a product using it's description (document) and probably hopefully other features such as price, weight, etc.The product description is quite short and could used in a standard TD-IDF approach, whereby each document (product description) is transformed into a sparse vector (fairly standard), and this is the feature.That is fine. However, I'm wondering the problems of simply adding other features on to this sparse vector -- such as price, weight, etc (obviously normalized). My hunch tells me that because these vectors will always be present in their respective dimension, that it may skew the learnings of an algorithm, or present other problems.I'm new to ML, so any hints/suggestions on how this scenario should be approached, would be hugely appreciated. I am wondering firstly if these additional features can simply be added on to the end of the sparse vector, and also how they should be normalized (with respect to the rest of that feature vector, or all other vectors, etc...?). 
Text classification & features: mixing sparse vectors (TD-IDF) with dense vectors (i.e., other features)
classification;feature extraction;multiclass classification;feature scaling