I have two vector-space models, with different dimensions.

The number of vectors in one model is the same as the number of vectors in the other. I.E: if I have vector representation for a car in one model, I have vector representation for a car in the other model, but the number of dimensions can be different.

I want to combine these models (and then cluster using the combined model), I cannot average (BoW) or add these models together as stated earlier they have different dimensions.

I was going to simply concatenate the vectors. Is this valid or is there better way to do this?

Also before you concatenate should you normalize?

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  • $\begingroup$ Won't it cause a problem if you have different dimensions while supplying the vector as an input to your model? $\endgroup$ – Aditya Apr 3 '18 at 7:38
  • $\begingroup$ If I simply stack the vectors then no, different dimensions imply different components so when stacking them, if I have a vector in R^2 and vector in R^3 the result will be a vector in R^5. $\endgroup$ – SFD Apr 3 '18 at 13:04
  • $\begingroup$ Like we do stacking for spare matrices, we need to have similar dimensions $\endgroup$ – Aditya Apr 3 '18 at 13:33

It is perfectly valid to concatenate the vectors from two different models. It will be necessary however to experiment with the two approaches, i.e. either using individual vectors from one of the models or concatenating the vectors, before you can determine which method will produce better clustering results. It is also valid to normalize word vectors, but again requires experimentation to determine whether this approach will provide superior results.

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