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I want to cluster my document vectors (doc2vec) using affinity propagation.

However, I am just confused if I should use cosine similarity or cosine distance to cluster my document vectors. Currently, I am using cosine similarity for my affinity propagation clustering. Thus, my first question is;

Is it correct to use cosine similarity to cluster my doc2vec document vectors?

Moreover, I would like to visualize my cluster results using t-sne. However, I saw that t-sne requires distance matrix as the input. Hence, my second question is;

Is it correct to use distance matrix (cosine distance) for t-sne, while I use cosine similarity for clustering?

If my code is required I can post it too.

Please help me.

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Both of them convert the distances back to similarities, albeit using different methods. They will, if I recall correctly, also square the distances.

This may be problematic with the most common variant of cosine distance, which already is a squared distance. So it may be a good idea to modify the methods to be able to directly work with the similarities. But you probably need to modify the source code for this (and understand the methods!)

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  • $\begingroup$ Thank you for your answer. Can you please tell me what is the standard inputs for affinity propagation and t-sne? (bacuase I do not have enough time to modify their source codes) $\endgroup$
    – Smith Volka
    Jan 4, 2018 at 11:15
  • $\begingroup$ Depends on what implementation you use. Some may accept multiple kinds of input. $\endgroup$
    – Has QUIT--Anony-Mousse
    Jan 4, 2018 at 19:15

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