Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels
for a given recognition task. A number of studies have shown that MKL is a useful tool for …

Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …

Understanding and interpreting classification decisions of automated image classification
systems is of high value in many applications, as it allows to verify the reasoning of the …

Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base
kernel matrices to improve clustering performance. However, existing MKC algorithms …

Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …

Alzheimer's disease (AD) will become a global burden in the coming decades according to
the latest statistical survey. How to effectively detect AD or MCI (mild cognitive impairment) …

In recent years, several methods have been proposed to combine multiple kernels instead of
using a single one. These different kernels may correspond to using different notions of …

Multiple kernel k-means (MKKM) clustering aims to optimally combine a group of pre-
specified kernels to improve clustering performance. However, we observe that existing …

Learning linear combinations of multiple kernels is an appealing strategy when the right
choice of features is unknown. Previous approaches to multiple kernel learning (MKL) …

The kernel method, especially the kernel-fusion method, is widely used in social networks,
computer vision, bioinformatics, and other applications. It deals effectively with nonlinear …