Motivation. Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of
a training gene expression profiles (GEP) ensemble, but it cannot
distinguish relations between the different factors, or different
modes, and it is not available to high-order GEP Data Mining. In
order to generalize ICA, we introduce Multilinear-ICA and apply it to
tumor classification using high order GEP. Firstly, we introduce the
basis conceptions and operations of tensor and recommend Support
Vector Machine (SVM) classifier and Multilinear-ICA. Secondly,
the higher score genes of original high order GEP are selected by
using t-statistics and tabulate tensors. Thirdly, the tensors are
performed by Multilinear-ICA. Finally, the SVM is used to classify
the tumor subtypes. Results. To show the validity of the proposed method, we apply it
to tumor classification using high order GEP. Though we only use
three datasets, the experimental results show that the method is
effective and feasible. Through this survey, we hope to gain some
insight into the problem of high order GEP tumor classification, in
aid of further developing more effective tumor classification algorithms.