A missing exponential part in the calculation of the quantity cv

[phiradet]

Dear all,

At the line number 161-162 of the file Vertex.scala, it is the implementation of the quantity c_v as described in the section 2.2 [1].

val ent = GetNeighborhoodEntropy(neighborClone)
var cv = math.log(beta) / math.log(beta + ent)

In the original paper, the entropy, denoted by the variable ent, is passed to a function f(x). The definition of the function is f(ent) = log(beta)/log(beta + e^(ent)). By the way, the line didn't implement the natural exponential part. I am just a beginner Scala programmer. I think it may be a mistake. Do you have any idea about this point?

Many thanks, Phiradet

[1] Talukdar, P. P., & Crammer, K. (2009). New Regularized Algorithms for Transductive Learning. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (pp. 442–457). Berlin, Heidelberg: Springer-Verlag.

[parthatalukdar]

On Wed, Feb 18, 2015 at 1:48 PM, phiradet notifications@github.com wrote:

At the line number 161-162 of the file Vertex.scala, it is the implementation of the quantity cv as described in the section 2.2 [1].

val ent = GetNeighborhoodEntropy(neighborClone)var cv = math.log(beta) / math.log(beta + ent)

In the original paper, the entropy, denoted by the variable ent, is passed to a function f(x). The definition of the function is f(ent) = log(beta)/log(beta + e^(ent)). By the way, the line didn't implement the natural exponential part. I am just a beginner Scala programmer. I think it may be a mistake. Do you have any idea about this point?

Thanks for pointing this out. The random walk probability formulas used in the paper are just one reference and by no means the only one. The user is welcome to substitute that with her own as long as they are valid probabilities.

Partha

[phiradet]

I got it. Thank you very much for your suggestion.

Phiradet