# Inverse tSNE is feasible?

Short question: is it meaningful to use tSNE ( http://homepage.tudelft.nl/19j49/t-SNE.html) to modify existing high-dimensional data using similarities in some low-dimensional vectors? In essence, that means applying tSNE in reverse direction ( but with constraint, that we are already given high-dimensional vectors and just wanted to "deform" them using the information in low-dimensional vectors ).

Long question: Here is my task : I have a list of word embeddings ( in word2vec sense ), and these vectors all are 64-dimensional.

On another hand, for each word a have another vector ( 15-dimensional ), and mapping is one-to-one ( i.e. each word have 64-dimensional embedding and 15-dimensional signature ). 15-dimensional vectors contain important additional information about words, which is impossible to take into account directly.

And I wanted to make these 64-dimensional vectors more similar by taking into account similarity between corresponding 15-dimensional vectors.