About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ISBI 2013
Conference paper
Multiple Kernel Completion and its application to cardiac disease discrimination
Abstract
Data is only as good as the similarity metric used to compare it. The all important notion of similarity allows us to leverage knowledge derived from prior observations to predict characteristics of new samples. In this paper we consider the problem of compiling a consistent and accurate view of similarity given its multiple incomplete and noisy approximations. We propose a new technique called Multiple Kernel Completion (MKC), which completes given similarity kernels as well as finds their best combination within a Support Vector Machine framework, so as to maximize the discrimination margin. We demonstrate the effectiveness of the proposed technique on datasets from UCI Machine Learning repository as well as for the task of heart valve disease discrimination using CW Doppler images. Our empirical results establish that MKC consistently outperforms existing data completion methods like 0-imputation, mean-imputation and matrix completion across datasets and training set sizes. © 2013 IEEE.