Centered Kernel Alignment (CKA)
#Data #Representation #Similarity
Centered Kernel Alignment (CKA) is a similarity metric designed to measure the similarity of between representations of features in neural networks^{1}.
Definition of CKA
CKA is based on the HilbertSchmidt Independence Criterion (HSIC) HilbertSchmidt Independence Criterion (HSIC) Given two kernels of the feature representations $K=k(x,x)$ and $L=l(y,y)$, HSIC is defined as12 $$ \operatorname{HSIC}(K, L) = \frac{1}{(n1)^2} \operatorname{tr}( K H L H ), $$ where $x$, $y$ are the representations of features, $n$ is the dimension of the representation of the features, $H$ is the socalled centering matrix Centering Matrix Useful when centering a vector around its mean . We can choose different kernel functions $k$ and $l$. For example, if $k$ and $l$ are linear … .
HilbertSchmidt Independence Criterion (HSIC)
But HSIC is not invariant to isotropic scaling which is required for a similarity metric of representations^{1}. CKA is a normalization of HSIC,
$$ \operatorname{CKA}(K,L) = \frac{\operatorname{HSIC}(K, L)}{\sqrt{\operatorname{HSIC}(K,K) \operatorname{HSIC}(L,L)}}. $$
Applications
CKA has Problems too
Seita et al argues that CKA is a metric based on intuitive tests, i.e., calculate cases that we believe that should be similar and check if the CKA values is consistent with this intuition^{2}. Seita et al built a quantitive benchmark^{2}.

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 ↩︎

Seita D. How should we compare neural network representations? In: The Berkeley Artificial Intelligence Research Blog [Internet]. [cited 8 Nov 2021]. Available: https://bair.berkeley.edu/blog/2021/11/05/similarity/ ↩︎
L Ma (2021). 'Centered Kernel Alignment (CKA)', Datumorphism, 11 April. Available at: https://datumorphism.leima.is/cards/machinelearning/measurement/centeredkernelalignment/.
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