TD-Based Unsupervised FE

In the previous chapter, I have introduced PCA-based unsupervised FE as a tool that can identify features having favorable properties without pre-knowledge, e.g., class labeling and period. In this chapter, I introduce TD-based unsupervised FE as a natural extention of PCA-based unsupervised FE toward tensors. In contrast to PCA that can deal with only one feature, TD can deal with multiple features, e.g., gene expression and miRNA expression simultaneously associated with the same samples. If we consider case I and case II tensor approaches, we can perform integrated analysis of multi omics data sets, too. In addition to this, we further introduce additional strategies to integrate multiple omics data sets suitable for more than two profiles.