Parametric Models for Mutual Kernel Matrix Completion

Mutual Kernel Matrix Completion

With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods. However, among the many data completion techniques available in the lite...

Protein Classification via Kernel Matrix Completion

The three-dimensional structure of a protein provides crucial information for predicting its function. However, as it is still a far more difficult and costly task to measure 3D coordinates of atoms in a protein than to sequence its amino acid composition, often we do not know the 3D structures of all the proteins at hand. Let us consider a kernel matrix that consists of kernel values represent...

Kernel Matrix Completion by Semidefinite Programming

Non-parametric Bayesian Kernel Models

1 SUMMARY Kernel models for classification and regression have emerged as widely applied tools in statistics and machine learning. We discuss a Bayesian framework and theory for kernel methods, providing a new rationalisation of kernel regression based on non-parametric Bayesian models. Functional analytic results ensure that such a non-parametric prior specification induces a class of function...

The em Algorithm for Kernel Matrix Completion with Auxiliary Data

In biological data, it is often the case that observed data are available only for a subset of samples. When a kernel matrix is derived from such data, we have to leave the entries for unavailable samples as missing. In this paper, the missing entries are completed by exploiting an auxiliary kernel matrix derived from another information source. The parametric model of kernel matrices is create...