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I am trying to implement a Laplacian SVM classifier (trained in primal) using algorithm from this paper.

I would like to know what is the most common way of constructing adjacency matrix and the most common weights to put in the matrix. By most common I mean something that give acceptable results on average.

In the paper the author talks about k-nearest neighbors or graph kernel as examples, I have also seen distance with threshold (what threshold value leads to acceptable average results?). For the weights he talks about binary weights and heat kernel weights (I don't know others).

Thank you.

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