  Recent technology and equipment advancements provide with us opportunities to
better analyze Alzheimer's disease (AD), where we could collect and employ the
data from different image and genetic modalities that may potentially enhance
the predictive performance. To perform better clustering in AD analysis, in
this paper we propose a novel model to leverage data from all different
modalities/views, which can learn the weights of each view adaptively.
Different from previous vanilla Non-negative Matrix Factorization which assumes
data is linearly separable, we propose a simple yet efficient method based on
kernel matrix factorization, which is not only able to deal with non-linear
data structure but also can achieve better prediction accuracy. Experimental
results on ADNI dataset demonstrate the effectiveness of our proposed method,
which indicate promising prospects of kernel application in AD analysis.
