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Found 9 papers, 0 papers with code
A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets
In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.
Ecological Data Analysis Based on Machine Learning Algorithms
In our study we conclude that Linear Discriminant Analysis and k-nearest neighbors are the best methods among all other methods
Gene Shaving using influence function of a kernel method
Identifying significant subsets of the genes, gene shaving is an essential and challenging issue for biomedical research for a huge number of genes and the complex nature of biological networks,.
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.
Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.
Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis
Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.
Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis
Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets.
Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
Finally, we propose a method based on robust kernel CO and robust kernel CCO, called robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.
