Statistics – Machine Learning
Scientific paper
2009-09-07
Statistics
Machine Learning
Work in progress, in particular lacks references to very recent literature (2007/2008/2009) as this paper was submitted and re
Scientific paper
We present in this work a new family of kernels to compare positive measures on arbitrary spaces $\Xcal$ endowed with a positive kernel $\kappa$, which translates naturally into kernels between histograms or clouds of points. We first cover the case where $\Xcal$ is Euclidian, and focus on kernels which take into account the variance matrix of the mixture of two measures to compute their similarity. The kernels we define are semigroup kernels in the sense that they only use the sum of two measures to compare them, and spectral in the sense that they only use the eigenspectrum of the variance matrix of this mixture. We show that such a family of kernels has close bonds with the laplace transforms of nonnegative-valued functions defined on the cone of positive semidefinite matrices, and we present some closed formulas that can be derived as special cases of such integral expressions. By focusing further on functions which are invariant to the addition of a null eigenvalue to the spectrum of the variance matrix, we can define kernels between atomic measures on arbitrary spaces $\Xcal$ endowed with a kernel $\kappa$ by using directly the eigenvalues of the centered Gram matrix of the joined support of the compared measures. We provide explicit formulas suited for applications and present preliminary experiments to illustrate the interest of the approach.
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