Frobenius norm

A function to compute the Frobenius norm between two classifications as defined in Lajugie et al. 2014 and Arlot et al 2019

Frobenius(c1, c2, sorted_pairs = NULL)

Arguments

c1

A vector of length $n$ with values between 0 and $N_1 < n$ representing the first classification. Supported types: integer, numeric, or factor. Avoid character vectors for better performance. Must not be a list.

c2

A vector of length $n$ with values between 0 and $N_2 < n$ representing the second classification. Supported types: integer, numeric, or factor. Avoid character vectors for better performance. Must not be a list.

sorted_pairs

optional output of function sort_pairs (if already computed). If `NULL` (the default), will be called internally

Value

a scalar with the Frobenius norm.

References

- Rémi Lajugie, Francis Bach, and Sylvain Arlot. "Large-margin metric learning for constrained partitioning problems." International Conference on Machine Learning. PMLR, 2014. - Sylvain Arlot , Alain Celisse, and Zaid Harchaoui. "A kernel multiple change-point algorithm via model selection." Journal of machine learning research 20.162 (2019): 1-56.

See also

ARI, NID, NVI, NMI, clustComp

Examples

data(iris)
cl <- cutree(hclust(dist(iris[, -5])), 4)
Frobenius(cl, iris$Species)
#> [1] 2.21181