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Found 5 papers, 0 papers with code
A Fast, Consistent Kernel Two-Sample Test
A kernel embedding of probability distributions into reproducing kernel Hilbert spaces (RKHS) has recently been proposed, which allows the comparison of two probability measures P and Q based on the distance between their respective embeddings: for a sufficiently rich RKHS, this distance is zero if and only if P and Q coincide.
Kernel Change-point Analysis
Change-point analysis of an (unlabelled) sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating the change-point instant after which the distribution of the observations switches from one distribution to another different distribution.
DIFFRAC: a discriminative and flexible framework for clustering
We present a novel linear clustering framework (Diffrac) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem.
Catching Change-points with Lasso
We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise.
Testing for Homogeneity with Kernel Fisher Discriminant Analysis
This provides us with a consistent nonparametric test statistic, for which we derive the asymptotic distribution under the null hypothesis.
