sklearn.metrics.pairwise.additive_chi2_kernel¶
- sklearn.metrics.pairwise.additive_chi2_kernel(X, Y=None)[source]¶
Computes the additive chi-squared kernel between observations in X and Y
The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms.
The chi-squared kernel is given by:
k(x, y) = -Sum [(x - y)^2 / (x + y)]
It can be interpreted as a weighted difference per entry.
Read more in the User Guide.
Parameters: X : array-like of shape (n_samples_X, n_features)
Y : array of shape (n_samples_Y, n_features)
Returns: kernel_matrix : array of shape (n_samples_X, n_samples_Y)
See also
- chi2_kernel
- The exponentiated version of the kernel, which is usually preferable.
- sklearn.kernel_approximation.AdditiveChi2Sampler
- A Fourier approximation to this kernel.
Notes
As the negative of a distance, this kernel is only conditionally positive definite.
References
- Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. Local features and kernels for classification of texture and object categories: A comprehensive study International Journal of Computer Vision 2007 http://research.microsoft.com/en-us/um/people/manik/projects/trade-off/papers/ZhangIJCV06.pdf