Application of TD-Based Unsupervised FE to Bioinformatics

Although the purpose of data science is to understand something complicated, if all of the complicated things are understood, it might not be interesting. Thus, it is better for something complicated to remain not to be fully understood. In the previous chapter, we demonstrated that PCA-based unsupervised FE is applicable to a wide range of bioinformatics problems. Nevertheless, in some specific cases, TD is more suitable than PCA. There are two such possible situations. The first situation is that data itself should be formatted in tensor rather than matrix. The second situation is the integrated analysis of more than two matrices. In this chapter, we demonstrate in which situation TD-based unsupervised FE is better to be applied. Applications of newly added strategies to real examples are also included.