Testing for Homogeneity with Kernel Fisher Discriminant Analysis
We propose to test for the homogeneity of two samples by using Kernel Fisher discriminant Analysis. This provides us with a consistent nonparametric test statistic, for which we derive the asymptotic distribution under the null hypothesis. We give experimental evidence of the relevance of our method on both artificial and real datasets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Kernel Change-point Analysis
We introduce a kernel-based method for change-point analysis within a sequence of temporal observations. Change-point analysis of an (unlabelled) sample of observations consists in, first, testing whether a change in the…
Two-sample testingEssence of kernel Fisher discriminant: KPCA plus LDA
In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA).…
Fisher and Kernel Fisher Discriminant Analysis: Tutorial
This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA. We start with projection and reconstruction. Then, one- and multi-dimensional FDA subspaces are covered. Scatters in…
Dimensionality ReductionRoweis Discriminant Analysis: A Generalized Subspace Learning Method
We present a new method which generalizes subspace learning based on eigenvalue and generalized eigenvalue problems. This method, Roweis Discriminant Analysis (RDA), is named after Sam Roweis to whom the field of subspac…
Dimensionality ReductionFace RecognitionThe Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis
Fisher's linear discriminant analysis is a classical method for classification, yet it is limited to capturing linear features only. Kernel discriminant analysis as an extension is known to successfully alleviate the lim…