Hilbert-Schmidt Independence Criterion (HSIC)
Given two kernels of the feature representations
where
, are the representations of features, is the dimension of the representation of the features, is the so-called [[centering matrix]] Centering Matrix Useful when centering a vector around its mean .
We can choose different kernel functions
Gretton A, Bousquet O, Smola A, Schölkopf B. Measuring Statistical Dependence with Hilbert-Schmidt Norms. Algorithmic Learning Theory. Springer Berlin Heidelberg; 2005. pp. 63–77. doi:10.1007/11564089_7 ↩︎
Kornblith S, Norouzi M, Lee H, Hinton G. Similarity of Neural Network Representations Revisited. arXiv [cs.LG]. 2019. Available: http://arxiv.org/abs/1905.00414 ↩︎
cards/machine-learning/measurement/hilbert-schmidt-independence-criterion
:cards/machine-learning/measurement/hilbert-schmidt-independence-criterion
Links to:L Ma (2021). 'Hilbert-Schmidt Independence Criterion (HSIC)', Datumorphism, 11 April. Available at: https://datumorphism.leima.is/cards/machine-learning/measurement/hilbert-schmidt-independence-criterion/.