Generalized RBF kernel for incomplete data

Generalized RBF kernel for incomplete data

We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based...

RBF kernel method and its applications to clinical data

In this paper, basing our considerations on kernel-based approaches, we propose a new strategy allowing to approximate the prostate cancer dynamics. In particular, starting from several measurements of a specific biomarker, we estimate the tumor growth rate. To achieve this aim, we pre-process data via Radial Basis Function (RBF) interpolation. A careful choice of the basis function and of its ...

Consensus Kernel K-Means Clustering for Incomplete Multiview Data

Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view's incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and impu...

Image Reconstruction from Incomplete Data

New RBF collocation methods and kernel RBF with applications

Abstract. A few novel radial basis function (RBF) discretization schemes for partial differential equations are developed in this study. For boundary-type methods, we derive the indirect and direct symmetric boundary knot methods. Based on the multiple reciprocity principle, the boundary particle method is introduced for general inhomogeneous problems without using inner nodes. For domain-type ...